verbatim assignment model

What is Verbatim Text? Unveiling the Power of Exact Transcription

In the world of transcription and textual documentation, precision is often paramount. Whether you’re dealing with legal proceedings, interviews, research data, or content creation, having an accurate and unaltered record of spoken words is essential. This is where “Verbatim Text” comes into play. In this comprehensive guide, we’ll explore the concept of Verbatim Text, understand its significance, and discover how it’s utilized across various industries. Additionally, we’ll shed light on how Transcribe Lingo, with its expertise and advanced technology, can assist in delivering precise Verbatim Text services tailored to your needs.

Defining Verbatim Text

Verbatim Text , often simply referred to as “Verbatim,” represents a transcription style that aims to capture spoken words precisely as they were uttered, leaving no room for interpretation or alteration. In Verbatim Transcription, every spoken word, pause, stutter, filler word, background noise, and even non-verbal expressions like laughter or hesitation are faithfully transcribed into text.

To better understand Verbatim Text, let’s delve into its key characteristics:

1. Inclusion of Fillers and Non-Words

Verbatim Transcripts include fillers like “um,” “uh,” “you know,” and non-words such as “ah,” “eh,” and “mmm-hmm.” These elements, although not conveying substantial meaning, are integral to Verbatim Text as they mirror the speaker’s exact speech pattern.

2. Pauses and Silence

Even moments of silence and pauses, regardless of their duration, find their place in Verbatim Text. These breaks can be essential in understanding the speaker’s thought process or emphasizing certain points.

3. Non-Verbal Expressions

Verbatim Transcription goes beyond words, encompassing non-verbal expressions like laughter, sighs, and gestures. These elements provide context and emotional nuances to the transcript.

4. Background Noise

Any background noise, whether it’s the hum of an air conditioner, a passing vehicle, or a distant conversation, is documented in Verbatim Transcripts. This can be particularly relevant when analyzing the recording’s environment or context.

The Significance of Verbatim Text

Verbatim Text serves diverse purposes across various industries and professions:

1. Legal Proceedings

In the legal field, Verbatim Transcripts are invaluable. They provide an unaltered record of court proceedings, depositions, and witness testimonies, ensuring that every word spoken is accurately preserved.

2. Market Research and Interviews

Researchers often rely on Verbatim Transcripts to capture participant responses during interviews and focus group discussions. This level of detail aids in comprehensive analysis.

3. Content Creation

For content creators, Verbatim Transcripts can be the foundation for articles, blogs, or video scripts. They allow creators to retain the authenticity of spoken content.

4. Academic Research

In academia, Verbatim Transcripts are crucial for qualitative research. They enable researchers to analyze interview data in-depth, uncovering themes and patterns.

5. Medical and Healthcare

Verbatim Transcripts find applications in medical settings, recording doctor-patient interactions, telemedicine sessions, and medical dictations. Accuracy is paramount in healthcare documentation.

How Transcribe Lingo Assists with Verbatim Text

Transcribe Lingo is your trusted partner when it comes to Verbatim Transcription services. Here’s how we excel in delivering precise Verbatim Transcriptions:

Advanced Technology

We leverage cutting-edge speech recognition and Natural Language Processing (NLP) technologies to ensure the highest level of accuracy in Verbatim Transcripts.

Expert Transcribers

Our team consists of skilled transcribers who are well-versed in Verbatim Transcription guidelines. They understand the importance of capturing every spoken word faithfully.

Customization

We offer customization options to meet your specific needs. Whether you require Verbatim Transcripts for legal purposes or qualitative research, we tailor our services accordingly.

Confidentiality

We prioritize the security and confidentiality of your audio content. Our robust security measures ensure that your sensitive data remains protected.

We deliver Verbatim Transcripts within your specified timeframe, even for large audio files. Our efficiency ensures that you have access to accurate transcripts when you need them.

Verbatim Text is a transcription style that leaves no room for interpretation, aiming to capture spoken words exactly as they were uttered. Its applications span legal, research, content creation, academic, and healthcare fields, among others. The significance of Verbatim Text lies in its ability to provide an authentic and unaltered record of spoken content, making it an invaluable tool for professionals and organizations.

When precision is paramount, Transcribe Lingo stands as your reliable partner in delivering Verbatim Text services. With advanced technology, expert transcribers, customization options, and a commitment to confidentiality, we ensure that your Verbatim Transcripts meet the highest standards of accuracy and authenticity. Harness the power of Verbatim Text with Transcribe Lingo and elevate your transcription experience.

Have audio content that requires Verbatim Transcription? Contact us today to get started on your transcription journey!

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Michael Milton

Michael Milton and Faith for Living, Inc.

November 12, 2015

A Guide to Writing a Verbatim in Pastoral Care and Counseling Class

The following is a guide to help my students in the class I teach on  Pastoral Care and Counseling to write a Verbatim (theological reflection paper on a student-pastoral counseling case).

The Pastoral Care Verbatim is a document that chronicles the context of a ministry event—self, parishioner, presenting issues, dialogue—and contains the theological reflection of the minister as an after-action report.  

The Report follows a pattern of Readiness, Recording, and Reflecting. The Readiness stage of pastoral care verbatim invites the minister to record preparations for the pastoral care encounter. This includes Scripture, prayer, and any background information pertinent to the visit.

The Recording stage involves the dialogue documentation. This phase of the report involves the recall of the dialogue verbatim.   The speaker is identified as P for parishioners, C for clergy, and O for others (e.g., a nurse or aid comes into the room). The dialogue may be limited to entrance and dismissal or a sample from the movements of a pastoral event (entrance, Word, Table [or Response, remembering the ministry of Jesus Christ and, thereby, renewing our awareness of the presence of Christ], and Dismissal).

Begin with a self-assessment: your identity as a gospel minister, your awareness of your source of strength and the source of healing that you draw from for the person God has brought to you, and your attitude towards the person.

[perfectpullquote align=”left” bordertop=”false” cite=”” link=”” color=”” class=”” size=””]The dialogue may be limited to entrance and dismissal or a sample from the movements of a pastoral event (entrance, Word, Table [or Response, remembering the ministry of Jesus Christ and, thereby, renewing our awareness of the presence of Christ], and Dismissal).[/perfectpullquote]

Prepare for the Assessment, Diagnosis, and Treatment. Think through the Gospel-centered counseling ministry model of creation, fall, and redemption. Use differential counseling to refer as appropriate.

The Pastoral Care Verbatim is written as soon as possible after the experience. It will be seen only by yourself and me.

SELF-REFLECTION

How has my call prepared me for this ministry? What are my limitations? How is my prayer life today with the Lord? Am I coming into the presence of this lamb of the Lord fully prepared through prayer and communion with the Holy Spirit? Do I see this person as one who is made in the image of God? Do I understand my role as an ambassador of God seeking to offer redemption in His name? Am I bearing the emblems of Christ Jesus in my identity as a minister of the Gospel? Is the Lord Jesus my first and only real identity and source of healing? Have I prayed? Am I seeking Biblical metaphors, Gospel, and cross-centered ministry patterns as I approach this person?

ASSESSMENT, DIAGNOSIS

Did I greet the person in a way that made them feel welcome? Are the surroundings safe for this person with me? How do I feel about this space? Is it adequate for the ministry before us today? Did I begin by reminding the person of my role as pastor (not a psychologist or other mental health professional) if they didn’t know me? I am an ordained minister and provide Biblical counseling according to my faith and training as a minister of the Gospel of Jesus Christ. Did I ask if she wanted to proceed with that kind of counseling? Did we pray? Was my prayer seeking the Lord’s presence and His power?

In ASSESSMENT, did I model Christ’s ministry of looking and loving the whole person? Did I observe sensate signs during our initial moments together and verbal signals that might alert me to issues requiring immediate care? If so, did the needed care require professional attention (e.g., medical) beyond my credentials? Did I care for the counselee by urging such immediate care? What else did I notice in the assessment? What did I miss in the assessment?

How did the counselee describe the presenting issue? How would you restate it?

How did you frame your counseling approach with a Biblical fall scripture? With a Biblical redemption passage? Did the consultation lead you to another passage? Why? How did it work out during the time together?

[perfectpullquote align=”left” bordertop=”false” cite=”” link=”” color=”” class=”” size=””] The Pastoral Care Verbatim is a document that chronicles the context of a ministry event—self, parishioner, presenting issues, dialogue—and contains the theological reflection of the minister as an after-action report. [/perfectpullquote]

Where do I see God’s beauty of creation at work in this life? In this situation? How does God’s beauty still shine through the ashes of pain?

What presenting issues do I see that may be leading me to genuine issues? How is her relationship with her family? With her church community? With her neighbor?

How does the fall manifest itself in this soul? In these peoples’ lives? How has sin infected this community? Has the cancer of sin metastasized into other areas of this life? If so, into which areas? Have I identified the core area of sin? Am I listening well? Am I listening to her soul speak, or just her words? Has another hurt him? Has the wound been infected over time? Has the wound been neglected? Has the wound failed to heal by other means?

Where in the Gospel story does this life, this story before me, present itself? Where can I lead her to see Jesus traveling with her now in the Gospel story? How will prayer best be made manifest to this soul? Now? In the future? In her family? With friends? In a church community? How can the sacraments be used to bring healing to this person? Is he baptized? When did she last receive Communion? Did she tell you about her life of faith in Christ due to experiencing Communion? What devotional books could he be reading to help his life in Christ at the very point of fallenness? At other critical points of weakness? What simple habits of daily prayer would act as highly directed charges of spiritual chemotherapy to begin to shrink numerous sinful growths in personality or behavior?

Did I lead into a time of conclusion or get caught off-guard by the time? Did I pray? What words of comfort did I speak? What [rituals did I use that might have brought comfort to them? How did I convey my role as a pastor in healing ways to help speak the redeeming love of Jesus Christ into the woundedness of this situation? How did I conclude? Is there a time for a follow-up? What about his personal worship? Public worship? What about her devotional life? Did I help him establish patterns of spiritual health? Am I helping this person find sustainable spiritual formation for a lifetime following the Lord Jesus Christ?

WRITING THE VERBATIM

Remember that the movements of any pastoral event are the entrance, Word, Table (or Response in remembering the renewing faith in the resurrected Christ), and dismissal.

Frame the ministry event in the stages of a Pastoral Care Verbatim: Readiness, Recording, and Reflecting.

Begin with the Introduction to the “parishioner.” Identify notable traits. Write descriptively and concisely (e.g., “Mrs. Edna Jones is a Caucasian, female, of medium build. She appeared to me to be middle-aged.”). Identify the presenting issues (e.g., “Mrs. Jones is presenting that she and her husband argue every day.”). Record the verbatim this way:

I was called by a member of our church who is a neighbor of Mrs. Mary Jones. Mrs. Jones is in a post-retirement stage of life, is widowed, and lives alone. According to a member of our church, Mrs. Jones has enjoyed fine health. Since her husband died, she has been a sporadic church attended, and the lack of fellowship has made an impression on her neighbor, our member. I pull into the hospital clergy parking lot. I leave the car running as I pray. I am drawn to the God who will never leave or forsake us. I read the passage from Hebrews 13:5: Let your conduct be without covetousness; be content with such things as you have. For He Himself has said, “I will never leave you nor forsake you” (NKJV). This will be my text for the visit. I walked through the movements of a visit in my mind (entrance, Word, Table or Response, and Dismissal). I will shape the visit around Hebrews 13:5. I sat for just a few moments, focused on the passage, and thanked God for the opportunity to be His ambassador. I left my car and headed up the hill to the hospital. I noticed the dogwoods were in bloom.

[Minister/Chaplain is “C.” Parishioner is “P.” Patient is “P.” Others, e.g., nurses, are “O” for others. Use a system that makes sense to identify family members or others present during your ministry event.]

I greet Mrs. Mary Jones in room 202. She is in pain. I am determined to be careful about my time with her. I prayed, “Lord, help me to be there to encourage her with Your presence. Help me to see when I should depart.”

We talked about Mrs. Smith, our member, and how she and Mrs. Jones have been neighbors for twenty years. We talked about our church. She told me that she is Lutheran but that “I have not been faithful in attendance since my husband, Jim, died.”

[The Recording picks up at Word.]

C1. Mrs. Jones, how has the arguing shaped your spiritual life?

P1. What do you mean? Do you mean my private devotion?

C2. Yes. Your devotional life, your true, inner life with God?

P2: I am farther from God than I have been in many years.

C1: Mrs. Jones, I have a passage I prepared for you in mind. May I read it? It is only one verse.

I did not administer Communion. I did ask her to remember that Christ lived the life we could never live and died a death of atonement for sin. I spoke slowly and softly as I assured her that Christ is with her now. I asked her to remember His promise, “I will never leave you . . .” and asked if I could pray. We prayed and closed with the Lord’s Prayer.

After assuring her that I was here for her if she needed me, I asked if I could lay my hands on her head and pray for her. I pray that Mrs. Jones may be given eyes of faith to discern His presence. I close with a brief benediction. I end with, “Is there anything I can do for you?” I departed as nurses began coming in for medications.

Theological Reflection followed. I recorded my visit and began the process of theological reflection with the Residency Team. I submitted my Verbatim to the learning management system for archiving.

A Word on Writing the Theological Reflection

Theological reflection begins with identifying presenting issues of a ministry event and seeking Biblical understanding and pastoral application. One may also frame the theological reflection on assessment, diagnosis, and treatment: (1) theological issues involved in the treatment; (2) how your initial approach (your choice of Scripture, your approach in the Creation-Fall-Redemption motif) might have been different, if at all, given the interview; (3) self-reflection in the interview (e.g., transference, use of your own spiritual experience of God, insights from your life) and (4) final thoughts and recommendations, strategies, homework assigned, or closing thoughts on the case.

A structured approach for qualitative verbatim analysis

Using examples of Net Promoter Score data from two studies - one of patients assessing their primary care physicians and the other from the consumer electronics industry - the authors explore strategies for extracting insights from large qualitative data sets.

Three steps to clarity

Editor’s note: Michael Feehan is co-founder and CEO, and Penny Mesure is a director, of Observant LLC, a Waltham, Mass., research firm. Cristina Ilangakoon is a senior statistical analyst at CTB/McGraw-Hill, a Monterey, Calif., publisher of assessment solutions for the education markets.

To quote Benjamin Franklin, “By failing to prepare, you are preparing to fail.” While this aphorism is frequently used in the sporting world by coaches to reinforce the necessity of practice before a competition, it can easily apply to the world of qualitative research. If we as qualitative market researchers do not gather our data and prepare it for review in a way that is conducive to analysis, we are in effect preparing for an arduous and inefficient (read stressful) analysis process, and run the risk of missing the mark in generating powerful insight. Taking the time to structure one’s data before diving in to any analytic process can be invaluable.

In general, qualitative data (of whatever form) is gathered, structured and then analyzed with the aim of developing themes and drawing associations between those themes to advance understanding on the phenomenon under investigation. Such qualitative research data may come in many forms, whether transcripts of one-on-one or group interviews, transcripts of online bulletin boards, observational data in ethnographic studies or verbatim responses captured in the context of quantitative studies.

In applied or consulting settings this analytic process must often be conducted in a milieu where: a) the objectives are typically set in advance; b) the aims are set by the information needs of the funding body; c) time frames are limited; d) there is often a need to link the data to other quantitative information, and e) the raw data can be extremely voluminous. In the  British Medical Journal , Pope et al., (2000) propose an inductive “framework approach” that reflects the accounts and observations of those studied but involves a more structured data collection process than is seen in some other forms of qualitative research and leverages an analytic approach more strongly informed by a priori reasoning. This process generally involves the steps of familiarization (immersing oneself in the raw data); identifying a thematic framework; indexing (coding with text descriptors); charting (beyond grouping verbatim text and incorporating researcher abstraction and synthesis); and finally mapping/interpretation (interpreting the phenomenon and providing explanations). This is a useful heuristic that provides a map for effective qualitative research and analysis in the commercial sector, even if individual qualitative market researchers may use differing terminology or jargon.

In this process, researchers are essentially engaged in a reductive process, refining and distilling what can be very large volumes of data into manageable units for subsequent analysis and interpretation. In large multi-site qualitative studies (e.g., doing market opportunity assessments in a cross-national study) the volume of data generated through purely qualitative interviews can be enormous. Similarly, in large N quantitative studies which allow for open-ended responding, the market researcher can be faced with several thousand responses that need to undergo a reductive process to abstract key insights of relevance to clients. Rather than hand off an Excel file of verbatims to a data processor for coding (simply grouping verbatims under topic headers), we recommend an approach whereby the qualitative researcher first structures and examines the data, prior to establishing some kind of thematic coding frame.

We recently conducted a quantitative study of the perceptions consumers hold about their primary care physicians and tested alternate questionnaire design approaches to measure attributes describing doctors and the salience of these attributes to their patients. As part of this project we included an assessment of the likelihood to recommend a doctor, both quantitatively through a rating scale and the calculation of the industry standard Net Promoter Score (NPS) and also through the collection of open-end verbatim responses accounting for that prior rating.

Here we describe an approach to analyzing these qualitative verbatim data that was later leveraged in a large multi-country study of customer loyalty in the consumer electronics market.

A cardinal measure

Across companies as diverse as American Express, eBay, Jet Blue Airways, Symantec, Verizon Wireless, Apple, Amazon, P&G and Merck, executive teams increasingly rely on Net Promoter Score as a cardinal measure of customer loyalty and a key indicator to measure and track their brand performance over time.

This measure, described in author Fred Reichheld’s  The Ultimate Question as a “foolproof test” (p. 18) highlights the proportion of promoters of the brand relative to the detractors of the brand, in terms of their response to a single question on a 0-10 point scale: “How likely are you to recommend this company/product to a friend or colleague?

In May 2009 we conducted an online survey of 394 respondents representative of the general U.S. population and asked them: “How likely would you be to recommend your primary care doctor to a family member or friend who was looking for a new doctor?” Using standard criteria, respondents were classed as promoters (P: 9 and 10), neutrals (N: 7 and 8) or detractors (D: 0 through 6). NPS was calculated as NPS = P - D. Our sample contained 61 percent promoters and 17 percent detractors. The NPS for a family doctor in the U.S. is therefore +44, a number that many commercial organizations would love to achieve (albeit rather below the figure expected for a luxury sports car).

Open-ended responses were then gathered to “explain why you gave this rating.” This question resulted in 342 verbatim responses from the 394 respondents. Some of these verbatims were sparse and others more verbose; and on cursory examination some addressed a single concept (e.g., “She understands me.”), while others addressed more than one (e.g., “a nearby office and friendly staff”).

The database was structured and the verbatims analyzed in three stages. The key goal was to use standard tools in most analytic software (SAS, SPSS, Excel) to break down the verbatims into manageable units of text that would allow for speedy review by a researcher.

Step 1: Assessing the volume of response

Prior to exploring the substantive nature of these verbatims, we first addressed a hypothesis that those who would be more likely to promote or detract their physician, would simply have more to say. That is, a quick metric to gauge “strength of feeling” would be the simple volume of total text associated with each response. To do this, the number of characters used in each verbatim were calculated (using a function in SAS), as were the number of words used.

Both metrics confirmed our hypothesis in that promoters and detractors said more than those in the neutral category. On average promoters used 17 words (70 characters) and detractors used 19 words (76 characters) while neutrals used only 13 words (55 characters). This makes sense: satisfied patients are bubbling over with good things to say, while those who would not recommend their family doctor justify their position at length. Neutrals were just neutral. We are not aware that word/character counting is a standard feature of NPS analysis, but this quick metric was beginning to give us a picture of who these respondents are and their strength of feeling about their doctors (or what would be brands in other contexts).

Step 2: Scoring favorability of each unit of analysis

Each verbatim was then broken down into the separate text units, comprising a different idea or aspect of the verbatim, using punctuation delineators (i.e., periods, commas, colons/semi-colons, along with and/&), with the exception of periods after “Dr.” Despite similar levels of volume in aggregate, there were significantly more text units per promoter (2.4) than per detractor (1.8). Promoters were more likely to laud their doctor with multiple reasons. Detractors were giving fewer overall reasons, but were using more words to get things off their chest about a single dimension of their doctor’s behavior or style.

Step 3: Qualitative coding of themes

Baseline assessment

We recently applied this approach for one of our clients in the consumer electronics industry. We conducted a baseline assessment of NPS across nine brands, with around 150 consumers reporting on each brand, across seven countries. This gave us 9,450 survey responses. While not every respondent provided a verbatim response, since some verbatims comprise multiple text units and ideas, the volume of responses that could inform strategic strengths and weaknesses for the brands is considerable.

To aid this analysis we created a structured database, in a fashion similar to the one described above, that allowed us to review and analyze the verbatims quickly and without undue burden on analyst staff. Without going into the detail of this company’s NPS and process metrics, we were able to produce high-value reports efficiently that yielded key insights into drivers of loyalty.

Analysis of the nearly 10,000 respondents’ qualitative data revealed that among promoters sound quality was one brand’s key strength, though durability and value for money also emerged as important: “It has the highest sound quality and it is reasonably priced. Also, the durability and longevity of the product are the best of any audio equipment I have ever owned. I am a fanatic!”

Analysis of the detractor verbatims highlighted the chink in this brand’s armor as being cost: “It is only an average value for the money you pay. If they were to decrease the prices and/or increase the quality, I’d be more likely to recommend.”

May seem like overkill

To some qualitative market researchers, this multi-stepped and (at least in the initial stages) pseudo-quantitative approach may seem like overkill. Especially when the number of open-ended responses is comparatively few and the task of identifying themes (across aggregate verbatim responses) may not be too challenging.

However, there are two major advantages to doing this. First, in many corporate NPS studies the sample sizes (and resultant volumes of verbatims) can be simply staggering. For example, in the construction equipment rental market, Peterson (2008) cites researcher Ellen Steck at RSC Equipment Rental, who obtained 23,000 completed customer surveys per year. Larger corporations may generate many times that number.

Many companies may simply focus on the quantitative NPS scores generated and, in the absence of some structured approach, neglect the value of a qualitative analysis of their often times un-analyzed verbatims. Key levers for positive change may thus be missed. In these cases some form of computational algorithms should be used to reduce and structure the verbatim data in order to minimize research time and do analyses as efficiently as possible. Second, some of the provisional metrics may themselves be useful data to track. An early indicator of improving fortune in the loyalty wars may be things like the volume of words customers say about your brand, the number of ideas they reference about your brand or the ratio of positive to negative ideas.

In terms of next steps it would be useful to analyze other NPS verbatim data and abstract text to develop positive and negative adjective batteries. This will allow researchers to search for text in the verbatims and code each as positive or negative (as opposed to manually by a researcher). While not perfect, this level of automation is necessary in studies where the researcher may be working with 30,000-40,000 verbatims. Once their direction is coded, subsamples of positive and negative verbatims can then be reviewed and analyzed by the research team for thematic content.

One interesting perspective that should not be overlooked in this approach really comes from an appreciation of the questioning style of qualitative researchers. The way open-ended responses are gathered in the industry-standard NPS assessment is to rely on the recommended question: “What is the most important reason for the score you gave?” (Reichheld, p. 33). This closed form of questioning can lead to simple lists of reasons without direction or strength of conviction (e.g., “The cost” or “Its quality”). We recommend that clients gathering NPS use an alternative: “Why did you give this score?” This simple change will encourage richer verbatims (e.g., “The terrific quality used to be worth the cost, but isn’t now with cheaper competitors available.”)

More efficiently focus

In sum, by proactively structuring the qualitative data using simple text-editing tools, and conducting preliminary counts of key verbatim types, the qualitative market researcher can more efficiently focus on what is critical - abstracting their key insights form very large verbatim sets.  

Peterson, L.M. (2008). “Strength in Numbers.” International Rental News , 8(3), 37-41.

Pope, C., Ziebland, S., Mays, N. (2000). “Qualitative Research in Health Care: Analyzing Qualitative Data.” British Medical Journal , 320, 114-116.

Reichheld F. (2003). The Ultimate Question: Driving Good Profits and True Growth . Boston: Harvard Business School Press.

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What is Verbatim Transcription? Verbatim Transcription Definition

A verbatim transcript captures every single word from an audio file in text, exactly the same way those words were originally spoken. When someone requests a verbatim transcription, they are looking for a transcript that includes filler words, false starts, grammatical errors, and other verbal cues that provide helpful context and set the scene of the scenario that was recorded.

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What’s the difference between verbatim and non-verbatim transcription?

There are two main types of transcriptions, verbatim and non-verbatim. Verbatim is where a transcriptionist types all of the words they hear including certain non-speech sounds, interjections or signs of active listening, filler words, false starts, self-corrections, and stutters. This type of transcript requires a ton of extra time and attention to detail and for this reason, costs a little bit extra.

Non-verbatim transcription, on the other hand, is cleaned up to remove filler words, stammers, and anything that takes away from the core message of what’s being said. This type of transcript is the most common and should be lightly edited by the transcriptionist for readability.

Here’s an example of two actual sentences transcribed non-verbatim and verbatim and compared side-by-side:

Example 1 Non-verbatim: I think we should go to the movies tonight because of the discount. Verbatim: And so, um, I guess… I think we should go to the, the m- m- movies tonight ‘cause of the discount (laughs).

Example 2 Non-verbatim: I called her yesterday and she was sleeping. Probably, she was just really tired. Verbatim: I like, you know, called her, like, yesterday and, um, like, she was, like, sleeping. Probably, she was just like, really tired.

When is verbatim transcription useful?

Verbatim transcripts provide helpful context that a cleaned-up transcript doesn’t offer. Because true verbatim transcripts include non-speech sounds like “mm-hmm (affirmative)” or “mm-mm (negative)” they are especially useful when conducting a focus group, quoting a source or requesting a legal transcription.

In most cases, these words aren’t necessary, but there are many instances when they provide helpful verbal cues such as in audio files of police interrogations where these types of verbal pauses might provide insight into a person’s demeanor.

Verbatim transcriptions should be used when:

• Directly quoting a source
• Conducting a focus group
• Interpreting interviews from a research study
• Preparing legal documents
• Delivering a legal statement

Which transcription service should you use?

One might think that a transcript is, well, a transcript — a written record of recorded audio. But that oversimplifies a bit. Depending on the type of transcript a customer orders, there will be some variations in the format and level of detail delivered by a transcriptionist. Timestamps, an instant first draft, rush delivery, and verbatim transcription are all among the add-ons a customer might request. Transcripts support a variety of different projects and depending on the cope of that project, you’ll want to make sure you’re ordering the right add-ons. Learn more about our transcription services and determine which offering is right for you.

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Verbatim vs. Intelligent Verbatim: Which Transcript Style to Choose, and When

September 09, 2021

Transcripts can be created in a range of styles and formats, depending on the scenario. Verbatim and intelligent verbatim are the two most common, but in order to choose the right one, it’s important to understand the differences between them.

When it comes to creating a written record of the spoken word, the finished transcript can vary greatly depending on the proceedings being documented, who the transcript is for, and how it will be used.

What Makes a Transcript ‘Verbatim’?

A verbatim transcript includes everything that’s said, exactly as  it’s said. So as well as the spoken word, the transcript also includes:

• pauses, silences, repetitions, stutters and stammers
• non-verbal ‘fillers’, such as ‘uh’ and ‘um’
• noises, including coughing and laughter
• physical gestures and movements, for example head shaking or nodding
• ambient noise, including background chatter, doors banging
• overspeaking and false starts
• slang, grammatical errors and non-standard language such as ‘aint’

In a verbatim transcript, there’s no cleaning up, summarizing or finishing off sentences, and no attempt to explain the meaning of what’s been said.

When Verbatim Transcripts are Useful

Verbatim transcripts are used for court proceedings as well as formal hearings that might lead to legal action, for example fraud or corruption inquiries, regulatory probes, police interviews, or employment court proceedings.

In these situations, it’s not just what’s said that matters, but how. By capturing gestures and mannerisms, a verbatim transcript can show how the speaker is thinking or feeling. Are they nervous or relaxed? Confident or hesitant?

Total accuracy is essential, and everything must be included – a failure to reflect the manner in which something was said could have serious consequences for the outcome of formal or legal proceedings.

There are other scenarios in which a verbatim transcript is useful. In transcripts dealing with medical or scientific data, cleaning up punctuation can change the meaning entirely. For market research companies running focus groups or businesses holding staff feedback sessions, non-verbal content is critical to understanding the real meaning behind the spoken word.

How are Intelligent Verbatim Transcripts Different?

An intelligent verbatim transcript is a ‘cleaned-up’ version of what’s been said. All redundant words or sounds are removed, as well as any non-verbal content.

However, when producing an intelligent verbatim transcript  the transcriber goes further, for example correcting grammatical errors or paraphrasing speech in order to make the meaning clearer or more succinct.

Unlike a verbatim transcript, an intelligent verbatim transcript only conveys the meaning of what’s been said, rather than how. The aim is to create a transcript that is more readable, and easier to understand.

Compare these two examples:

Verbatim:   “So, anyway, you know, I’m planning to start the, um, project in hmmm, let see, actually definitely on the, ah, the 10 th  of December. It’s a bit complicated, you see? So, eh, I plan to let all my funders know my kinda thinking so that they see I am very, very serious about taking off soon, you get it, right? Know what I mean?

Intelligent verbatim:   “I’m planning to start the project on the 10 th  of December this year. I plan to let all my funders know I am very serious about taking off soon.”

The Power of Intelligent Verbatim

There are many scenarios in which intelligent verbatim is more appropriate than verbatim.

Businesses wanting to document meetings or phone calls to capture and share key messages will find this type of clear, to-the-point transcript ideal. And organizations needing to share content from conferences and events will also find intelligent verbatim transcripts useful.

In fact, in any situation requiring concise, clear communication to a target audience, where the way in which things are said is not critical, this transcript style is the one to choose.

Although they sound similar, there are some important differences between verbatim and intelligent verbatim transcripts. By understanding the characteristics and advantages of each, you can make a more informed decision about which one is right for you.

For more information on Appen’s verbatim and intelligent verbatim transcription services please contact us .

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Get Smart: Understanding Intelligent Verbatim Transcription

Last Updated December 10, 2021

The priority with intelligent verbatim transcription is capturing the meaning of the data.

Verbatim means “word for word.” It’s one of multiple types of transcription that can be used depending on the goal of the document. Another is intelligent verbatim transcription, which takes context into account.

Transcribers who work straight verbatim take the audio and type absolutely everything said. They include every utterance, regardless of the level of importance or usefulness.

Verbatim transcription is indispensable in a legal setting, for example. It’s even handy if you’re trying to settle an argument. Wouldn’t we love a verbatim transcript when we’re trying to figure out who agreed to do the dishes earlier?

For most situations though, straight verbatim transcription isn’t that useful. That’s where intelligent verbatim transcription becomes useful.

What is intelligent verbatim transcription?

Let’s say you’re turning a presentation into a written report. Your CEO gave a talk at a conference with a big PowerPoint component.

She’s an engaging speaker, but she does say “you see” quite a bit. At one point she accidentally clicked too far ahead in her slides, then stopped to apologize and get herself organized. Also, she had a cold and spent about half a minute explaining her scratchy voice at the beginning of the presentation.

This is where “intelligence” comes into the picture.

Elements intelligent verbatim transcription will skip:

• Filler words like ‘um’ ‘so’ and ‘you know’, which act like conversational grease but make your written content cluttered and confusing
• Repeated sentences that might sound great when delivered live, for emphasis, but are unnecessary for reading
• Digressions and other irrelevant or off-topic content which will lessen the impact on the page

Again, the priority with intelligent verbatim transcription is uncovering the meaning of the data. So, the transcriber may also use their best judgement to do things like insert punctuation and correct obvious grammar errors to make the text more readable.

This is partly why human transcription is still needed . When clients come to us for  speech data collection  and  transcription , they’re trying to solve for the edge cases where automatic speech recognition (ASR) still struggles – whether it’s recognizing a greater variety of accents, dealing with background noise, transcribing conversational data , or cutting out examples noted above.

Are you looking for more information about verbatim transcription, or other ways of transcribing content ? Shoot us a message. We would love to help you out.

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Are verbatim transcripts necessary in applied qualitative research: experiences from two community-based intervention trials in Ghana

1 Institute for Global Health, University College London, 30 Guilford St., London, WC1N 1EH UK

Charlotte Tawiah-Agyemang

2 Kintampo Health Research Centre, P.O. Box 200, Kintampo, Ghana

Betty Kirkwood

3 London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT UK

Carl Kendall

4 Department of Global Community Health and Behavioral Sciences, Tulane University School of Public Health and Tropical Medicine, 1440 Canal Street, Suite 2350, New Orleans, LA 70112 USA

Associated Data

Conducting qualitative research within public health trials requires balancing timely data collection with the need to maintain data quality. Verbatim transcription of interviews is the conventional way of recording qualitative data, but is time consuming and can severely delay the availability of research findings. Expanding field notes into fair notes is a quicker alternative method, but is not usually recommended as interviewers select and interpret what they record. We used the fair note methodology in Ghana, and found that where research questions are relatively simple, and interviewers undergo sufficient training and supervision, fair notes can decrease data collection and analysis time, while still providing detailed and relevant information to the study team. Interviewers liked the method and felt it made them more reflective and analytical and improved their interview technique. The exception was focus group discussions, where the fair note approach failed to capture the interaction and richness of discussions, capturing group consensus rather than the discussions leading to this consensus.

The value of qualitative research within public health trials and programmes is increasingly recognized [ 1 , 2 ], as demonstrated by its prominence in the United Kingdom Medical Research Council framework for the development and evaluation of complex interventions [ 3 ]. When done well qualitative research can improve the design, conduct, interpretation and transferability of intervention trials [ 4 ]. This is most likely to happen when qualitative research is integral to the trial rather than peripheral or an add on [ 2 ].

Conducting integrated qualitative research within intervention trials requires balancing the need to make findings available to the team in a timely manner, with the need to maintain data quality. One of the most time consuming components of qualitative research is the transcription of interviews. Verbatim transcription, a word for word reproduction of the interview, is the convention, and is considered to enhance the rigour and accuracy of the data [ 5 , 6 ], but can severely delay the availability of research findings [ 7 ].

This paper highlights the lessons learnt in two large scale trials, conducted at the Kintampo Health Research Centre, Ghana, using expanded field notes, fair notes [ 8 ], to record data rather than verbatim transcription. Fair notes save time and capture the main topics of interviews, but are considered less accurate [ 7 , 9 ]. During the two trials we learned valuable lessons about enhancing the quality of the fair note method. This paper outlines the rational for choosing fair notes rather than verbatim transcription, and our experiences using the method.

The trials were conducted between 2000 and 2010. The Obaapvita trial tested the impact of Vitamin A supplementation on maternal mortality [ 10 ]; and the Newborn Home Intervention Trial (the Newhints trial) tested the impact of home visits by community health workers on neonatal mortality [ 11 ]. Within these trials, qualitative research was conducted prior to the trial to inform intervention design and data collection plans, and during the trial to identify emerging implementation issues, and to conduct process evaluations to understand the reasons why the interventions were or were not successful. In addition, specific sub studies were conducted exploring issues such as informal abortions and women’s understanding of being in a trial. This qualitative work included in-depth interviews, focus group discussions and trials of improved practice and resulted in eleven peer-reviewed articles [ 12 – 23 ]. The core qualitative data collection methods are shown in Table ​ Table1. 1 . In many cases these qualitative data were complemented by quantitative data.

Description of the qualitative methods used in the trials

Rationale for using fair notes

When we planned the formative research to design the communication strategy to maximise compliance with weekly vitamin A/placebo capsules in the ObaapaVitA trial, we were faced with a decision about how to record interview data. We had three choices:

• Audio record and take field notes during the interview, and use the audio recording to produce verbatim transcripts and the field notes to add non verbal communication and observations.
• Audio record and take field notes during the interview, and use the recording to expand the field notes into fair notes.
• Take field notes during the interview and expand on these into fair notes from memory [ 7 ].

With the advent of portable audio recording equipment verbatim transcription had replaced fair notes as the convention [ 24 ], and audio recording was advised if a fair note approach was used [ 9 ]. However, the time demands of transcription were problematic for the ObaapaVitA qualitative research team, who needed to deliver usable data to the trial team within a few months. The simplicity and rapidity of fair notes was attractive, so the team compared these data recording methods according to seven criteria as shown in Table ​ Table2 2 .

Comparison of different data recording methods

Table ​ Table2 2 shows that, although verbatim transcription is not an error free objective replication of the interview, it is the most complete method of recording data. It captures the respondent’s language most accurately, and permits quality assurance for the content of the interview. However, the ObapaVitA team calculated that if transcription was conducted by the interviewers, it would almost triple the duration of the formative research, especially as the interviewers were not expert typists. This meant that contracting transcription out would be the only feasible option to ensure that the formative research was compatible with the trial timeline. As shown in Table ​ Table2 2 contracting transcription out is problematic because it introduces an additional potential source of error into the analysis. It also means the research team loses control of the transcription process, and the team does not benefit from the analytical thinking that occurs during note-taking and transcription.

The ObaapaVitA team opted for the fair notes approach. This was mainly driven by time constraints, our relatively simple research questions, and a desire to keep data recording within the field team to enhance reflection and analytical thinking. Having decided on a field note approach we were faced with a choice of audio recording the interviews and using the audio recordings to expand the interviews, or using memory to expand the interviews. At the time the recording equipment available for the study was bulky, and the population we were working with was not used to the equipment. We were worried that being recorded would make participants feel nervous and inhibited. Although we knew that audio recording was recommended [ 9 ] we decided not to record the interviews.

Given the problems with expanding notes from memory that are outlined in Table ​ Table2, 2 , we planned for intensive training and supervision during data collection to help ensure important content was not lost, that the interviewers recorded the language of the participant as much as possible, and to aid in reflective and analytical thinking. The next section describes our experiences and lessons learnt in implementing this approach first describing our experience using the approach with interviews in the ObaapaVitA trial, the adaptation of the approach for the Newhints trial, and finally our experience using the approach for recording focus group discussions.

Use of fair notes in the ObaapaVitA trial

Data were collected by five interviewers across the different rounds of qualitative research. During a 1 week training on qualitative methods and on the study we discussed writing up using the fair notes method. This component of the training focused on:

• The importance of the research for the trial and of the trial itself. This was included to motivate the interviewers to write detailed fair notes.
• The intent of each question on the interview guides, so that interviewers understood why the question was being asked and would be able to identify relevant interview content to include in their notes.
• The importance of capturing the voice of the participants rather than their own voice, but that fair notes should say ‘she said that she went to the shop…’ rather than ‘I went to the shop’ to be clear that it is not a verbatim transcript.
• The importance of interviewers recording their own thought and reflections, but that these should be clearly recorded in the fair notes using brackets [….].

The qualitative team then conducted several practice interviews, initially with each other, then with staff at Kintampo Health Research Centre and finally in the community. The senior project researchers leading the training workshops observed the practice interviews. Interviewers completed practice fair notes and discussed the notes line by line with a senior researcher. The interviewers then added to or changed their notes based on the discussion. The research team then met as a whole to review key lessons learnt about note taking and key findings from the interviews. During data collection we continued with one to one feedback and group discussions.

Lessons learnt using fair notes in the ObaapaVitA trial

The practice interviews and write-ups identified three key problems with the fair note approach: taking detailed notes during the interviews, ‘tidying up’ fair notes and writing up brief fair notes.

Interviewers initially took very detailed notes during the interview, as they were fearful of forgetting things. This impacted on rapport building with the participant and meant that interviews were overlong. Senior researchers worked with the interviewers to help them trust their memories and interviewers practised taking concise, less obtrusive notes. Interviewers found that if they wrote up their interviews as soon as they returned to the field office, between 1 and 3 h after interviews were completed, they could easily remember what was discussed. This enhanced their confidence, reduced the length of the notes they took during the interviews and also increased the speed at which they wrote up their interviews. We developed a pattern of going to the field sites (1–2 h drive) early in the morning, conducting on average two interviews a day per fieldworker and then returning to the office to immediately start the write up which usually took the rest of the day. It was logistically easier to conduct two interviews a day, but this meant that interviewers had to rely more on their notes when converting the second interview into fair notes, whilst the write up of the first interview was always started between 1 and 3 h of data collection.

Interviewers wanted to make their fair notes read well, it was common for them to use technical terms that we knew were rarely used in the community. Whenever we saw such terms we used it as an opportunity to discuss ‘capturing the voice’ of the participant, for example we discussed the term ‘high blood pressure’, and found that the respondent had actually said ‘blood was up’. Through these discussions interviewers learnt that we were interested not just in what the participants said, but in capturing the words they used to say it.

Initially, despite detailed field notes, the fair notes were relatively brief summaries of the interviews, sometimes even bullet points extracted from more complete field notes taken during the interview. It took time for the interviewers to determine what a good fair note should consist of, this came through discussion and by asking interviewers to read each others write-ups. Discussing the intent of each question, and reviewing and discussing the fair notes, helped the interviewers understand the content that we were interested in. For example, we would read the fair notes and discuss why something was important for the study, and whether the respondent had said any more about the issue. Additional information would be added to the notes. We found that over one to two weeks the interviewers became aware of important content and the fair notes became longer and more detailed. We also found that the interviewers began to feel less like interviewers applying an interview guide and more like investigators—this enhanced the quality of the data in that interviewers probed more and were more likely to ask follow up questions on key issues, this was an unanticipated advantage of the approach. Interviewers who had previously transcribed interviews, reported that writing fair notes made them more reflective about their interviewing style, biases and perceptions and made them think analytically. They reported that this made writing up more enjoyable and enhanced their interviewing skills, they felt this increased their future employability and motivated them to do a good job.

It was important that interviews were conducted, written up, reviewed and corrected the same day when memories were fresh. Initially the process was quite slow, but as confidence grew and feedback reduced in length the process sped up, and interviewers were conducting and writing up an average of two interviews a day. The time it took to get to this stage varied by data collector but on average it took around two weeks. Keeping the interviewers motivated to complete quality write-ups was important. Senior researchers reviewing fair notes and going to the field with the interviewers showed that they cared about the study, and discussing findings made their use and importance clear. Interestingly interviewer motivation dipped at the same time data saturation was reached, as the interviewers complained that they were not learning new things and started to lose interest, this did not affect the quality of the fair notes, but interviews became shorter with fewer probes which was addressed through the feedback loop. Using the fair notes method, the research took 8 weeks of high intensity work, with an additional week of training. When the team reflected on the experience we felt that collecting data intensely over a short period of time enabled the interviewers to become immersed in the topic and maintain interest compared to our previous experience with transcription.

Adaptation of the approach for the Newhints trial

From our experiences with the ObaapaVitA trial we were satisfied that, given the type of qualitative research questions asked within a trial, we could get useful data using the fair note approach. Small and compact recording devices had now become available to the study team, and the study population had become used to devices such as mobile phones. Given these changes we decided that we should maintain the fair notes approach, but audio record the interviews to allow interviewers to check for missed content, check language, to add key verbatim quotes and to allow for quality checks.

Based on one of the senior researcher’s experience with using audio recordings to expand field notes (ZH), we encouraged interviewers to first write fair notes from memory and then listen to the recording to check for completeness and to add key quotes-this was a quick process for the experienced interviewers with one hour of interview taking 1.5–2 h to write up. In practice most of the interviewers found it difficult not to rely on the audio recording, and despite several discussions almost all the interviewers listened to the recording and wrote up their notes as they went, this was a slow process and meant that interviewers could conduct and write up only one interview every day and a half. Data collection and analysis for the formative component of Newhints trial took longer per interviewer than for the ObaapaVitA trial. It also meant that interviewers were less likely to complete their write up as soon as they returned to the office as they knew they had the audio recordings to rely on at a later date. This meant that, at times, interviews were written up several days after the data were collected, which inhibited iterative data collection, interviewer reflection and disrupted the flow of the data collection-write up cycle.

Using fair notes to record focus groups discussions

Both trials used focus group discussions as one of the data collection methods. Interviewers found these difficult to write up using the fair note method. They tended to record group consensus rather than the discussions that led to consensus being reached. We did not learn much from this data collection method, as the data were too summarized and not at all rich compared to the in-depth interview data. From the formative research for the ObaapaVitA trial, we realised that fair notes were not a good way of capturing focus group discussion data. For subsequent focus groups and for the Newhints trial all focus groups discussions were audio recorded and transcribed verbatim. Focus groups were a much richer and a more useful data source in this study, compared with in the ObaapaVitA trial, as the content and nuances of discussions were captured.

This paper adds to the few papers that provide practical advice on fair notes and transcription within qualitative research [ 5 ]. We found, as have others, that where research questions are relatively simple, and interviewers undergo sufficient training and supervision, fair notes can decrease data collection and analysis time [ 7 , 9 ]. Fair notes have been criticised for resulting in simplistic interpretations that underreport the participants’ words [ 7 , 8 ], but we found that with training and supervision they can provide detailed and relevant information to the study team, and can enhance the quality of interviews and analysis—which has not been previously reported. The exception was data collected through focus group discussions, which was very difficult to write up using the fair notes approach. As others have found, writing up while memories are fresh is beneficial [ 25 ], however this may need to be balanced with ensuring feasible fieldwork logistics.

Researchers that plan to use the fair note method must factor training and timely supervision into timelines and staff costs, as it can take a week of training and up to two weeks of intensive supervision for data collectors to become proficient with the method. Using the fair note approach, allowed the team to iterate steps and findings during data collection and think reflectively and analytically. Interviewers reported that writing fair notes improved their interviewing skills. Although the data were rich and relevant, it may be that the completeness and accuracy of the data are low.

Using audio recording to expand field notes allowed verbatim quotes to be added and the completeness of write-ups to be checked, and is recommended [ 9 ]. However, we found that interviewers relied on the tape recordings, and that this increased the write-up time and decreased reflection and analytical thinking. There is no consensus on how audio recording should be used to write fair notes [ 5 , 9 ]; our experience supports listening to the audio recording after the field notes have been expanded from memory, but this may face resistance from interviewers as the existence of the tape made the fieldworkers reluctant to rely on their memories.

Acknowledgements

The trials were both part of a long term collaboration between the Kintampo Health Research Centre (KHRC), Ghana Health Service and the London School of Hygiene & Tropical Medicine. The authors would like to thank these collaborating partners for their support and gratefully acknowledge all members of the qualitative research interview team and all those community members who generously gave their time and provided information needed to support the trials. Finally we thank the LSHTM Centre for Evaluation for funding the writing workshop at which this paper was developed.

Author contributions

ZH drafted the text, which was reviewed by CTA, BK and CK. ZH, CTA and CK were involved in the implementation of the fair note approach within the two trials; BK was the principal investigator of both. All authors read and approved the final manuscript.

This study was supported in part by a Paper Writing Grant to Professor Betty Kirkwood from the Centre for Evaluation at the London School of Hygiene and Tropical Medicine, and by Bill and Melinda Gates Foundation grants OPPGH5297 and OPP1138582 through the World Health Organisation. The funders had no role in the conceptualisation, writing or findings of this paper.

Availability of data and materials

Declarations.

The authors declare that they have no competing interests.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Zelee Hill, Email: [email protected] .

Charlotte Tawiah-Agyemang, Email: ku.oc.oohay@gnameyga_ettolrahc , Email: [email protected] .

Betty Kirkwood, Email: [email protected] .

Carl Kendall, Email: ude.enalut@lladnekc .

Psychotherapy Verbatim Transcription Guide

A while back I wrote a post about general verbatim transcription convections that we use.

However, I am always open to creating a verbatim transcription rules for clients. Recently a psychotherapist got in touch looking for a verbatim transcription services of a counseling session using the psychotherapy transcription standards by Mergenthaler and Stinson (1992).

The first step was to create a transcription guide that followed the standards published in 1992, then updating the guide for 2021. Luckily, there were very little adaptation that needed to be made, and the client had followed our advice (check out this post ) and recorded high quality audio.

Here’s the guide that we created, closely following Mergenthaler and Stinson (1992), and hopefully it is of use to you.

What To Transcribe?

Verbal utterances: All words spoken as whole words or parts of words are to be reproduced in standard spelling. Dialect forms should be transcribed in their corresponding standard spelling forms. For example, if an English speaking person’s usual speech sounds like the following:

P: I know she ain’t gonna gimme lotsa trouble.

it should be transcribed using standard English spelling as follows:

P: I know she ain’t going to give me lots of trouble.

Note that the word “ain’t,” although substandard, is retained in its standard dictionary spelling. For transcribing instances where a speaker deliberately uses dialect forms signaling emphasis or humor, see below.

Paraverbal Utterances . All sounds or sound sequences serving as conversational gap fillers, expressions of feelings of doubt, confirmation, insecurity, thoughtfulness, and so on in English are written in the following standard spelling whenever possible (modified from Dahl, 1979):

• Affirmative: mm-hm, uh-huh, yeah, yup
• Negation: huh-uh, nah, uh-uh, hm-mm
• Noncommittal: hm, mm
• Hesitations: ah, eh, em, er, oh, uh, urn
• Questioning: eh, huh, oh
• Humor: ha, haha, ho, hoho
• Exclamation: ach, aha, ahh. bang. boom, ech, kerbang, oh. ooh, oops, ow pooh, pow, uch, ugh, wham, whew, whomp, whoo, whoops, whoosh, whop, wow

Additions to this list might be needed.

Nonverbal Utterances. All other noise-producing actions of the speaker are to be recorded where they occur in the text in the form of simple comments within parentheses:

P: (sneeze)(cough) well (sigh), I guess I caught a cold (laugh).

Noises Occurring in the Situational Context : Any other sounds produced by the situational environment are indicated within simple comments:

P: later when I (telephone rings): do you need to answer that?

Pauses. You may use a single dash character surrounded by spaces ( – ) to indicate a pause of approximately one second. Multiple dashes should be separated by spaces. Pauses of greater than approximately 5 seconds should not be indicated with dashes, but should be timed and indicated using the following coded comment form:

P: I can think of – -, nothing (p:00:03:35) nothing at all.

The example above indicates a pause of approximately 2 seconds and a second pause of 3 minutes and 35 seconds.

SPECIAL TRANSCRIPTION MATTERS

Incomplete Words. Word particles generated by word break, including stuttering and stammering are to be indicated by the word fragment followed by a hyphen(-) and a space. A broken word is defined as an incomplete word that is not repeated:

P: whenev- I can never visit them alone.

Stuttering is defined as: 1 ) one or more word particles, each sharing the initial letters of the following completed word; or 2) a sequence of more than one word particle, each particle sharing initial letters, but not followed by the completed word:

P: sh- sh- she t- t- t- asked me not to call her again

Indecipherable utterances. A single slash (/) is entered in the transcript for every utterance that cannot be clearly comprehended but can be distinguished as a separate word. A slash marking an incomprehensible word may be followed with a coded comment of the form “(?:word)” to indicate possibly correct words. Thus the”?:” indicates that the comment contains a word or words that may have been uttered by the speaker:

P: I was /(?:alone) there all I(?:night) / until be / / /.

If one cannot determine the number of words in an utterance or any of the possible words, this should tie simply indicated with the following comment:

P: (incomprehensible)

Quotations . If the speaker directly quotes prior discourse, the text for each speaker is enclosed in single forward quotation marks (‘), which is the same character as the apostrophe:

P: I asked ‘will you do it?’ and be yelled ‘stop talking to me like that’ and slammed the door.

Changes in Manner of Speaking. If the speaker changes his or her usual manner of speaking and uses a voice differing from the usual way of speaking, the words are enclosed between double quote character (“). In such double quoted text, slang and literal transcription may be used.

P: she tells me not to say “yawl come back now” and “gimme that” what does she think this is grammar therapy?

Punctuation . Punctuation markers are used to help the reader reconstruct the original flow of speech. They are not used according to traditional grammatical rule, because normal speech is rarely so well-ordered. The transcriber should use punctuation marks to indicate changes in the way of speaking. emphasis, intonation, and cadence. When in doubt, punctuation marks should not be used. Punctuation markers are always placed at the end of a word and should not split a word. The following situations are differentiated:

• Completion of a thought . The clear period (.) indicates the end of a completed thought and is usually accompanied by a drop in pitch.
• Broken thought. The semicolon (;) indicates a broken thought, followed by another thought for example:

P: I hate the way you; did I tell you about the wedding?

• Hesitation. The comma (,) indicates a hesitation followed by a continuator of the same thought and is usually accompanied by a slight drop in pitch, for example:

P: you, netter seem, to look at me when I am talking.

• Question. The question mark(?) indicates a question. usually accompanied by a rise in pitch, or a clear rise in pitch. It should be used at the end of possible questions indicated by a rise in pitch even if the statement does not contain a clear grammatical question form:

T: Do you dislike it when he does that?

P: I should like! it when he does that?

• Emphasis. The exclamation mark (!) immediately follows words clearly emphasized by the speaker as in the prior and following examples:

P: that may not matter to him! but I do not! like it.

Note that the exclamation mark in transcription is used only for emphasis and does not indicate the end of a grammatical sentence.

• Lengthened pronunciation. The colon (:) is not used in its traditional grammatical way but is used to indicate protracted or extended pronunciation of a word as in the following example:

P: Well: I never really: liked that much anyway.

FORMAL AND STRUCTURAL ASPECTS

Transcript Heading. The transcript should contain a header. The following example shows the types of information that one may wish to include. The entire set of information should be enclosed in parentheses as a comment:

(SUBJECT ID: 105, SESSION NO: 32 DATE: 29.SEP. l986, THERAPIST; Dr. Smith, TEXT TYPE: psychoanalytic session, Version No: 1.0)

Speaker Codes. Each turn of speech begins on a new line and is preceded by a code indicating the speaker. Speaker codes are of the format Xn: wherein X is a single letter indicating the speaker’s role and n is an optional digit ( if there is more than one speaker of a certain role). If n is omitted it is assumed to be the digit 1. Thus, in the following example;

T: how did that make you feel?

P1: I felt confused and angry.

P2: you never told me you were angry about that.

the first speaker T is a therapist and P1 and P2 are two patients. The speaker code T has an implicit digit component of 1 and is therefore the same as T1. This format can handle monologue, dialogue, individual therapy, group therapy, and single therapists or cotherapists.

A comment after the transcript header can be used to clarify the role of speakers, for example:

(P =Son, P2 =.Mother, PJ =Father, Tl =Therapist, T2 = Cotherapist)

Capitalization. With the exception of proper or personal names or the first person pronoun “I,” all words including the first letter of a sentence begin with lower-case letters. This enables the use of even the simplest word-counting programs.

Simultaneity: Simultaneous speech presents special problems, both for comprehension and for representation of text. For two speakers however this can be easily handled by inserting a plus sign ( + ) at the start of simultaneous speech and continuing transcription of the initial speaker until simultaneity ends. This is followed by the entire simultaneous speech of the second speaker and terminated by another “+”. The remainder of the non simultaneous speech is to be transcribed in its natural order. In the following example, the words “refused again” and “yes you” were spoken at the same time:

P: I was going to give John the map but he +refused again

T: yes you + have told me this once before

Compound Words. Compound words with standard hyphenated spellings are connected by hyphens without spaces:

P: I found the picture taped upside-down on the wall with a band-aid

Neologisms. Neologisms are spelled as best as possible. Words that are created by stringing other words together should be represented with hyphenation:

P: all this gaming-it-out is confusing me.

Word Division at the End of a Line. If the text is for computer-aided text analysis, words should not be split at margins using hyphens (this creates problems for some computer-aided text analysis tools); the word should be typed in full on the next line.

Contractions. The apostrophe (‘) should be used to indicate contractions:

P: it’s not fair that they’d get to go and I wouldn’t

Text analytic systems can then treat the two parts separated by the apostrophe as separate words (e.g., wouldn’t becomes wouldn, which can be treated as would, and t, which can be treated as not). If a contraction produces ambiguous parts, either the words should be spelled out completely or else the ambiguous parts should be followed with a slash and the clarifying word (or words connected by a hyphen without spaces as described above) as in the following example:

P: he’d/had not done it and he’d/would never do it

In the first case d stands for had and in the second case d stands for would. Without the additional information following the slashes the two d’s would be processed as the same word. If’s is not clarified, it should be assumed to represent the word would. Do not use the apostrophe to indicate aphesis (the omission of letters at the beginning or end of a word). The word ’cause, for example, should be spelled out in its standard English form because. Do not use the apostrophe to indicate the possessive case. Instead of such forms as Mary’s and John’s one should transcribe a follows:

P: that coat is Marys and this one is Johns.

Plurals. The apostrophe should not be used to indicate plurals of letters, numbers, acronyms, or abbreviations. The underscore can be used for clarity, if necessary:

P: he always got As because he was the teachers pet

P: she only types lower case a_s because her typewriter is broken.

Abbreviations. With the exception of formal titles abbreviations are not to be used unless the speaker verbally spells one. Periods are not used in abbreviations; use a space instead:

P: Mrs Smith thinks I made a terrible mistake, for example

P: and it irritates me that Jane always says “e.g”.

Numbers, Fractions, and the Like. Numbers and fractions are written out in full where possible. Only typical figures such as dates are transcribed as numbers. The “abbreviations for ..ante meridiem” and “post meridiem” should be capital letters without spaces (AM and PM):

P: in 1981 I saw the first two-thirds of a James_Bond_007 film at eleven-thirty PM for two dollars and fifty cents.

Mistakes. Slips of the tongue and other mistakes are to be transcribed in full:

P: I couldn’t stand the guilt, uh quilt she gave me for my birthday.

Correct Spelling. Spelling should follow Webster’s standards.

Where several marking rules apply, it is necessary to include them all in sequence, with a period or question mark going last:

P: he screamed ‘don’t shoot until you see the whites of their eyes’!.

Some Things To Avoid . Do not use a sequence of periods (…) to indicate ellipsis. Do not use special characters (such as { } ) unless needed for special purposes of your own.

ADDITIONAL AND OPTIONAL RULES

The following set of rules can be of help for research settings with special need.

Names. If confidentiality is an issue, pseudonyms may replace personal names, names of places and other identifiers. To signify that a name has been changed, precede it with an asterisk (*) without an intervening space. It is proposed that a separate list of substituted words be maintained and used consistently throughout all material transcribed for the same speakers:

P: *Jane told *Fred all about *Elliot and *Mary.

If more than one word is needed to replace a single word, the multiple substituted words should be joined by underscore characters (-) without intervening spaces. This enables the entire substitution to be counted as a single word in the case of subsequent computer text analysis:

P: *Albert changed his name and moved to *small_southwest_town.

If a title is to be used before a name. it should be separated from the name with a space. Apostrophes should be omitted from names containing them; hyphenated names should retain the hyphens. Names (even those not substituted by pseudonyms) should be joined with underscores to form a single entity:

P: Mr *Arnold_O_Malley wants to be on Hollywood_Squares and meet Eva_Gabor.

Date and Time Coding. The date, time of day, and elapsed times of a transcript may be inserted using special coded comments.

• Session date: The session date is indicated with a coded comment of the following form:

(d:10.JAN.1986)

The d: indicates the comment is a session date. The date is entered in the format “DD.MON.YEAR.. (a two digit representation of the day of the month, a three-capital­ letter abbreviation of the month, and a four-digit representation of the year, separated by period without spaces). Thus “(d:06.MAR.1986)” represents “March 6, 1986” The session date should he placed at the top of the session transcript just after the heading (note that the form of this code makes it accessible to computer systems). If the exact date is unavailable, the unknown information should be replaced by zero’s

• Time of Day: The beginning of session time is indicated with a coded comment in the following example:

(t:10:02:15)

The t: indicates the comment is the actual time of day of the session, if available. All*time codes are in the format “HH:MM:SS” (two-digit representations of hour, minute, and second each separated by a colon). (Some facilities may also allow the notation of Video frames also. in which case the time codes would be in the format “HH:MM:SS:FF”; if this is used, it should be clearly indicated in a simple comment at the beginning of the transcript.) Thus “10:02:I 5” represents 2 minutes and 15 seconds after the hour of 10 O’clock. It is preferable to use 24-hour clock time. The session time should be placed at the first of the session transcript on the line following the session date. If the exact time is unavailable, the unknown information should be replaced by 0’s.

• Elapsed Time. It is often helpful to insert elapsed time codes in a transcript. The relative time within a session is indicated with a ended comment of the following form:

P: we saw the movies (+:00:03:00) after dinner.

The”+:” indicates the comment contains the elapsed time since the beginning of the session. The “00:03:00” indicates this is the start of the third minute following the beginning of the session. If the minute changes in the middle of a word, the time code should be placed before that word. The interval between relative time codes (if they are to be used at all) depends on the nature of the study. For example, these codes can be used to relate the text to other temporally ordered data (e.g. physiological recordings). These might be placed at the beginning and end of specific events or they might be placed at regular intervals. such as every whole minute or every 5 minutes.

Ambiguity. Some statements may be ambiguous in print yet unambiguous when heard in a sound recording. It is to the advantage of both computer-aided analysis and human readers to convert such ambiguous utterances into unambiguous ones. A clarifying alternative word may be placed behind a slash (/). Alternatively, a number placed immediately after the slash can be used to indicate the index number of a word’s meaning in a specific content-analytic dictionary. In the case of ambiguous pronouns, it is possible to name the antecedent behind the slash or to include several words connected by hyphens (this rule is primarily for use during the verification and scientific annotation phases of transcript preparation):

P: we/group thought he/James_Joyce had ignored it/rules-of-the-game.

Segment Demarcation. Various segmentations of the transcript may he accomplished by using coded comment structures to indicate the start of a segment “(s:CODE)” and the end of a segment “(e:CODE)” These two are used to bracket a segment of type indicated by “CODE,” e.g., “DREAM.” Whatever word is substituted for “CODE” must be spelled exactly the same in both start-segment and end-segment coded comments. It is permissible for different segment types to overlap or embed. This approach can be used for many types of segmentation. One might segment by relationship episodes and dreams, as in the following example:

P: (s:RE) When I told *Jane that last I dreamed (s:DREAM) I was a butterfly (e:DREAM) she laughed (e:RE).

The coded comments “(s:RE)” and “(e:RE)” indicate the beginning and end of a relationship episode, respectively. The coded comments “(s:DREAM)” and “( e:DREAM)” indicate the beginning and end of a dream description. Segment demarcations of the same types must not overlap or be embedded. This will not usually be a problem.

COUNSELING TRANSCRIPT EXAMPLE

I have included the following mock interview formatted according to the transcription standards described above.

Several examples of problems typically encountered in preparation of psychotherapy transcripts for research and education purposes are shown:

(SUBJECT ID: 105, SESSION NO:1, DATE 9.JAN.1988)

(d:11.JAN.1988)

(t:11:03:00)

(T = Dr. Jones, P = John Doe)

T: can you, do you recall similar episodes in your /(?:adolescence) of / /? (microphone drops) you um, mentioned that uh an important person for you during your adolescence was your school teacher Miss *Green.

T: is there a particular incident that stands out in your mind or ( + :00:01:00) interaction between the two of you that stands out +in your mind that

P: urn I think that I can+ credit her for – – sort of turning me around uh academically, you know, because I was pretty much of a, I would not work real hard. and I think that um there was always a recognition that I had some potential um to do well in school, but never did well.

T: ( + :00:02:00) mm-hm.

P: and uh my brother was absolutely brilliant and everybody loved him and he was valedictorian, and so I had to sort of uh come in his shadow through grammar school and high school, you know, because we went to the same schools. so this Miss *Green. she’d/had been his teacher. I remember her very clearly, I can picture her face very clearly. um and she decided, I guess, that I was not going to slide anymore. like t- to fool around a lot, you know.

P: uh passing notes, goofing off, uh doing ( +:00:03:00) things, I mean, not serious. but Miss *Green, she knew all this, I’m sure. well. it carne grade time for the first marking period, and I knew enough never to get a C. and this one marking period she gave me two Ds!, uh ( + :00:04:00) one D in math um

P: so she gave me a D, and I was just terrified. um she handed the report card t- to me and looked at me urn for a long time. I remember that, and she made the D in red, in uh really thick: red: letter!’.

T: (incomprehensible)

P: and uh so I was just ( +:00:05:00) really flabbergasted. I didn’t know what I was going to do, because I knew to get a s- D was just awful.

T: mm-hm. do you remember what you were feeling a- a- at the time?

P: uh oh this really sinking feeling. like (child-like voice) “oh no”.

P: I had really, really screwed up and didn’t know how 1 was going to get out of it. (p:00:00:40) (sigh) ( + :00:06:00 ) uh it just seemed like the worst thing in the world that could happen. um and I knew my parents were going to be upset. and I knew that um it would be hard to undo that, you know. so I was feeling really, really um now I was afraid, um I was really dejected by it, I mean, that uh that she had done this. because I, actually m-, I should mention, I didn’t feel I deserved a D.- – – I think she gave it to me to motivate me.

T: mm-hm. do you remember; what do you think, was going on in her ( +:00:07:00) mind at the ti-.

P: yeah, I r- I remember, because I said she looked at me for a long time when she handed my report card to me. her saying um, like around, it was after the report card that urn that she said ‘you: are not! going to get away with doing no work ‘ and and that I was really going to have to do well.

P: uh to get grades in her classroom. and she reited, reiterated that again. and at the ( +:00:08:00) time I thought she was just the most stern. unreasonable person um, I mean, I do recall um really after that urn not liking her and uh. so I do think that she, but uh but see I think what she was doing was something really um very caring and very positive. I mean she had singled me out, I think, or maybe she did other people too, to really just get them on the ball.

References:

Mergenthaler E, Stinson CH. Psychotherapy transcription standards. Psychotherapy Research 2(2) 125-142, 1992

Dahl. H. ( 1979 ). Word Frequencies of Spoken American English. Essex CT: Verbatim.

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• User's Guide
• Browsing the Data Repository
• Using the TMS Lite Browser

Searching for Verbatim Term Assignments (VTAs and VTIs)

Verbatim Term Assignment (VTA) Searches scan only the verbatim term levels of dictionaries, and return information about the verbatim term itself, dictionary terms to which it is classified, and specifics about the VTA.

To search for verbatim terms:

In the Exploration tab, select Verbatim Term Assignment, then choose a simple or advanced search. See Perform a Simple Terminology Search or Perform an Advanced Terminology Search .

Analyze the Results of a Verbatim Term Search

Select a verbatim term record.

After you choose the term you want to examine, you can perform any of the following tasks with that verbatim term record:

Browsing a Dictionary Hierarchy

Examining a Term's Details and History

Examining a Relation's Details and History

This section includes:

Perform a Simple Verbatim Term Search

Perform an advanced verbatim term search, viewing source data for a verbatim term record, viewing a source data record with markup, viewing data from an external drill-down function or view.

Parent topic: Using the TMS Lite Browser

Simple Searches return all the verbatim terms whose Term Names contain the entire text string that you specify. For example, a Simple Search for ache returns the terms "backache" and "ache in back" but not the term "aching back." You can also include in your search string any of the text operators available in the inter Media text option; see Using Special Characters in Searching .

You can perform Simple Searches for data within one dictionary and domain, or expand the search across all domains and accessible dictionaries in the Repository. If you need to search for verbatim term data using details other than the term's name, or you want to perform more complex searches than just one text string, follow the instructions in Analyze the Results of a Verbatim Term Search .

Like the other windows in the TMS Lite Browser, the Terminology Search page is dynamic: when you choose a setting in one field, it may update the choices available in other fields in the page. For example, if you choose English from the Language list, the page refreshes and only lists English dictionaries in the Terminology list.

To perform a Simple Verbatim Term Search:

• If you are not in the Verbatim Terms Search page, click the Verbatim Term Assignment tab.
• From the Language list, choose the language for the dictionary (or dictionaries) you want to search. When you select a language, the Search page narrows the list in the Terminology list to dictionaries in that language.

All Terminologies to include all accessible dictionaries of the current language in your search.

The name of the dictionary to restrict your search to a single dictionary.

When you choose a specific dictionary, the Search page may change in several ways. Your dictionary choice can also effect which domains are listed in the Domain field. The Terminology Search restricts the values in this list to those domains that contain the selected dictionary.

• From the Domain list, choose a single domain to focus your search, or All Domains to search across every domain that includes the selected dictionary or dictionaries.
• Click Search .

The Verbatim Term Search window displays the terms that match your search criteria in the lower part of the window. See Analyze the Results of a Verbatim Term Search for the next step.

Parent topic: Searching for Verbatim Term Assignments (VTAs and VTIs)

Advanced Verbatim Term searches allow you more flexibility and power than Simple Searches, because they allow you to search for:

Current or retired verbatim terms

All verbatim terms that classify to a particular dictionary term

Verbatim terms that match external system criteria

To start an Advanced Verbatim Term Search, open the Verbatim Term Search page, then click the Advanced Search button. The Advanced Search page contains the following sections:

Terminology

Verbatim term, external system.

The Terminology selections restrict the Candidate Data Set for your Verbatim Term Search according to dictionary (labelled "Terminology"), domain, and data currency.

Parent topic: Perform an Advanced Verbatim Term Search

The Verbatim Term selections enable you to focus your search using the following fields relating to verbatim terms. The following selections can refine your Verbatim Term Search:

Use Context Search . If selected, the TMS Lite Browser will use the Context Server Index for your search. See Querying in Windows for an overview of the differences between simple and context searches. This page includes a tip with the common context search operators, such as Fuzzy and Soundex.

Use VT as DT . If selected, the search returns only direct matches, where the verbatim term (VT) and dictionary term (DT) match exactly.

Only Inconsistent . If selected, the browser will only return verbatim terms that are inconsistently classified in different domains.

Search ID . If a search object created this VTA or VTI, you can search for it using the values in this list.

Approval . The approval status of the VTA. Choose Approved , Not Approved , or All .

User . The user who classified the VTA or VTI.

Database . The external system database from which the source term originates.

Settings in the Relations section enable you to include a wider range of verbatim terms in your search, based on dictionary structure.

Searching for verbatim terms under a Start Level enables you to include all the verbatim terms that derive up to a particular verbatim term. If you search for "nerv%" in MedDRA and specify System Order Class (SOC) as the Start Level, the browser returns all verbatim terms that derive up to the "Nervous system disorders." If you include a Reverse at Level setting as well, the system derives the terms that share the common parent at the level specified. You can also focus the verbatim terms returned in this search to primary relations only by selecting the Only Primary Rels box.

Figure 14-6 Figure 14-5 Start Level Example

The sample dictionary in Figure 14-5 illustrates a Verbatim Term Search with a Start Level. If a query uses Level I as the start level, and the query criteria match Term B only, the resulting verbatim Term Records would be the VT records whose primary derivable path goes up to Term B. The End Level for all VT searches is always the VT level. In this example, the search would return the VT Level terms N, O, and P.

Figure 14-7 Figure 14-6 Reverse at Level Example

For the same sample dictionary, Figure 14-6 illustrates how the browser conducts a Reverse at Level search for verbatim term data. Assume a Verbatim Term Search uses Level II as the Start Level and Level I as the Reverse Level. If Term C is the only match for the query criteria, the browser derives Term C's parent in the Reverse Level (Term A), then returns all of the verbatim term level records whose Level I derivable parent is Term A. Effectively, the search in this example finds all of the verbatim terms that derive up to term C and any of term C's sibling terms (in this case, it is only sibling is term D).

Selecting the Only Primary Rels box excludes all secondary relations from the Verbatim Term Search. Viewing just primary paths information can clarify the classification information for you, but could focus the search more than you need.

You can focus a Verbatim Term Search according to which external system data is included in the verbatim term. When you choose an external system from the list (Oracle Clinical, in this case), the page refreshes and includes that external system's columns.

When you complete either type of Repository Search, Simple or Advanced, the browser displays the matches at the bottom of the window.

Presentation of Results in the Verbatim Term Search Window

Refining your search.

Figure 14-7 shows the results of a Simple Search of the MedDRA Primary Path Dictionary across all domains for the string "pain".

Figure 14-8 Figure 14-7 Results in the Verbatim Term Search Window

By default, each search window in the TMS Lite Browser returns no more than 25 rows per page. You cannot change this rows-per-page setting, but you can change the maximum number of records the TMS Lite Browser can retrieve; to do so, update the variable OPA_UIX_MAX_ROWS in the TMS Settings window. See Customizing Defaults in TMS Windows Using TMS Settings for more information on using TMS Settings to control some default values in the TMS Lite Browser.

You cannot use an HTML Layout to change the set of columns displayed for Verbatim Term Searches. All Verbatim Term Searches use the columns displayed in Figure 14-7 .

Parent topic: Analyze the Results of a Verbatim Term Search

If your search did not yield any of the records you wanted, or produced too many matches, you can edit your search criteria to create a more inclusive or specific search. To refine your search, change any of your choices for the dictionary or domain, or enter a new search string in the Search field, then click the Search button.

When you update any of these lists, the change causes the Web browser to refresh the page, but this refresh does not requery the database for new results. When you complete your changes to the search criteria, you must click the Search button to execute the search.

When you find the verbatim term you want to examine from the records returned in your Verbatim Term Search, you can either view more detailed information about that term or browse its derivable path or paths in the dictionary hierarchy.

Example 14-2 Selecting a Verbatim Term

Verbatim Term Searches return records in the format below. This example shows the verbatim term "left ear pain" classified to the dictionary term "Ear ache." This classification applies in the global domain for the MedDRA Primary Path Dictionary.

Figure 14-9 Example 14-2 Selecting a Verbatim Term

Clicking either hyperlinked Term Name (the verbatim term or the dictionary term to which it is classified) launches the Term Details window for that term. The Term Details window displays three types of data about a term: all of the detailed information in the database about that Term Record, its derived path in its dictionary, and any related terms. See Using the Term Details Window .

Each verbatim Term Record includes a Source Data icon under the Source heading. Clicking this icon launches the Classified Source Data window, which you can use to view the original source data from a particular external system. Reading through the external system source data can provide information about the source term, such as the clinical trial project and study from which it arose.

To view a verbatim term's external system source data:

The TMS Lite Browser loads the Classified Source Data page, with two fields displayed: Application and Database .

• From the Application list, choose the external system you want to investigate.
• Choose the Database from which you want to retrieve external system data.

To view an external system record, click the Markup icon to the left of the Project column. The Source Data page for that record loads in the browser; see Viewing a Source Data Record with Markup for more information.

Some external system information is displayed as hyperlinks. These details have been generated by external system views or functions, and the hyperlinks launch a page that provides more information about the external system. See Viewing Data from an External Drill-down Function or View .

Markup reformats the presentation of a source data record so that the selected source term is hyperlinked, and appears prominently in the page.

Figure 14-10 Figure 14-8 Sample Source Data Record

Drill-down information enables you to see more about the program, study, or project generating the data record that contains your source term. Clicking any of these hyperlinks launches either the Source Term Data view or Data Function page.

Using Verbatims as a Basis for Building a Customer Journey Map: A Case Study

• Conference paper
• First Online: 06 November 2021
• Cite this conference paper

• Arturo Moquillaza   ORCID: orcid.org/0000-0002-7521-8755 8 ,
• Fiorella Falconi   ORCID: orcid.org/0000-0003-2457-2807 8 ,
• Joel Aguirre   ORCID: orcid.org/0000-0002-8368-967X 8 &
• Freddy Paz   ORCID: orcid.org/0000-0003-0142-1993 8  

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1498))

Included in the following conference series:

• International Conference on Human-Computer Interaction

2324 Accesses

Customer Journey Map is currently a very used canvas in the UX (User Experience) practice and design processes. Although it is widely discussed, both in the academic and industrial domains; practitioners still present questions about how to model this diagram. Customer Journey Maps are typically generated from the Personas technique, which is generally created by interviews or observations. On the other hand, many organizations employ a tool called NPS (Net Promoter Score). This tool generates both quantitative and qualitative data. The tool obtains expressions from the customer about the service or product called “Verbatim” about the qualitative data. These verbatims capture faithfully the event that took place when customers interacted with the financial products, services, systems, or channels. In that sense, we present a case study where a different approach is employed to build a Customer Journey Map about customers and their User Experience interacting with ATMs in a financial institution in collaborative sessions. In this sense, by applying this approach, we could map the touchpoints by analyzing verbatims. This way, verbatims could constitute a better source of information over interviews. The integration of the verbatim analysis in the CJM process could effortlessly scale as the data gathered from customers grows, promotes the sharing of knowledge inside the organization, and the culture of data-driven decision-making. In the end, we could obtain crucial insights and pain points that could generate new opportunities, requirements, and even new projects for the channel development backlog. We shared the results with a multidisciplinary audience with positive feedback, and they suggested that for new initiatives and analysis, to apply this new approach.

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Acknowledgment

We want to thank the ATM team in BBVA Perú for its support along with the research. In addition, we thank the “HCI, Design, User Experience, Accessibility & Innovation Technologies (HCI DUXAIT)”. HCI DUXAIT is a research group from the Pontificia Universidad Católica del Perú (PUCP).

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Pontificia Universidad Católica del Perú, Lima 32, Lima, Perú

Arturo Moquillaza, Fiorella Falconi, Joel Aguirre & Freddy Paz

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Moquillaza, A., Falconi, F., Aguirre, J., Paz, F. (2021). Using Verbatims as a Basis for Building a Customer Journey Map: A Case Study. In: Stephanidis, C., Antona, M., Ntoa, S. (eds) HCI International 2021 - Late Breaking Posters. HCII 2021. Communications in Computer and Information Science, vol 1498. Springer, Cham. https://doi.org/10.1007/978-3-030-90176-9_7

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NbAiLab / nb-whisper-base-verbatim like 0

Finetuned verbatim model..

This model is trained 200 additional steps on top of the model below. This makes it outputting only text in lowercase and without punctation. It is also considerably more verbatim, and will not make any attempt at correcting grammatical errors in the text

NB-Whisper Base Verbatim

Introducing the Norwegian NB-Whisper Base Verbatim model , proudly developed by the National Library of Norway. NB-Whisper is a cutting-edge series of models designed for automatic speech recognition (ASR) and speech translation. These models are based on the work of OpenAI's Whisper . Each model in the series has been trained for 250,000 steps, utilizing a diverse dataset of 8 million samples. These samples consist of aligned audio clips, each 30 seconds long, culminating in a staggering 66,000 hours of speech. For an in-depth understanding of our training methodology and dataset composition, keep an eye out for our upcoming article.

Verbatim Model

While the main models are suitable for most transcription task, we demonstrate how easy it is to change the output of the main model. The following models are trained 250 additional steps from the main models above, and might be suitable for more targetted use cases:

• Verbatim version : This lower-cased variant is more literal and suitable for tasks requiring detailed transcription, such as linguistic analysis.

Model Description

• Developed by: NB AI-Lab
• Shared by: NB AI-Lab
• Model type: whisper
• Language(s) (NLP): Norwegian, Norwegian Bokmål, Norwegian Nynorsk, English
• License: Apache 2.0
• Trained from model: openai/whisper-base
• Code Repository: https://github.com/NbAiLab/nb-whisper/
• Paper: Coming soon
• Demo: See Spaces on this page

How to Use the Models

Online demos.

You can try the models directly through the HuggingFace Inference API, accessible on the right side of this page. Be aware that initially, the model needs to load and will run on limited CPU capacity, which might be slow. To enhance your experience, we are temporarily hosting some models on TPUs for a few days, significantly boosting their performance. Explore these under the Spaces section on the Main Page .

Local Setup with HuggingFace

Alternatively, you can run the models locally. The Tiny, Base, and Small models are optimized for CPU execution. For the Medium and Large models, we recommend a system equipped with a GPU to ensure efficient processing. Setting up and using these models with HuggingFace's Transformers is straightforward, provided you have Python installed on your machine. For practical demonstrations, refer to examples using this sample mp3 file .

After this is done, you should be able to run this in Python:

Extended HuggingFace

Examining the output above, we see that there are multiple repetitions at the end. This is because the video is longer than 30 seconds. By passing the chunk_lengt_s argument, we can transcribe longer file. Our experience is that we get slightly better result by setting that to 28 seconds instead of the default 30 seconds. We also recommend setting the beam size to 5 if possible. This greatly increases the accuracy but takes a bit longer and requires slightly more memory. The examples below also illustrates how to transcribe to English or Nynorsk, and how to get timestamps for sentences and words.

Long transcripts:

Timestamps:

Word Level Timestamps:

Whisper CPP

Whisper CPP is a C++ implementation of the Whisper model, offering the same functionalities with the added benefits of C++ efficiency and performance optimizations. This allows embedding any Whisper model into a binary file, facilitating the development of real applications. However, it requires some familiarity with compiling C++ programs. Their homepage provides examples of how to build applications, including real-time transcription.

We have converted this model to the ggml-format model used by Whisper CPP binaries. The file can be downloaded here , and a q5_0 quantized version is also available here .

WhisperX and Speaker Diarization

Speaker diarization is a technique in natural language processing and automatic speech recognition that identifies and separates different speakers in an audio recording. It segments the audio into parts based on who is speaking, enhancing the quality of transcribing meetings or phone calls. We find that WhisperX is the easiest way to use our models for diarizing speech. In addition, WhisperX is using phoneme-based Wav2Vec-models for improving the alignment of the timestamps. As of December 2023 it also has native support for using the nb-wav2vec-models. It currently uses PyAnnote-audio for doing the actual diarization. This package has a fairly strict licence where you have to agree to user terms. Follow the instructions below.

You can also run WhisperX from Python. Please take a look at the instructions on WhisperX homepage .

Instructions for accessing the models via a simple API are included in the demos under Spaces. Note that these demos are temporary and will only be available for a few weeks.

Training Data

The training data originates from Språkbanken and the National Library of Norway's digital collection, including:

• NST Norwegian ASR Database (16 kHz) and its corresponding dataset
• Transcribed speeches from the Norwegian Parliament by Språkbanken
• TV broadcast (NRK) subtitles (NLN digital collection)
• Audiobooks (NLN digital collection)

Downstream Use

The models, especially the smaller ones, may exhibit occasional hallucinations and may drop parts of the transcript. They are designed to convert spoken language into grammatically correct written sentences, which might not always be word-for-word translations. We have made two extra model variant for users that want a different transcription style. We encourage users to try the models themselves to get a better understanding.

Bias, Risks, and Limitations

Using these models without adequate risk assessment and mitigation could be considered irresponsible. They may contain biases or other undesirable distortions. Users who deploy these models or integrate them into systems or services are responsible for mitigating risks and complying with applicable AI regulations. The National Library of Norway, as the model owner, disclaims liability for any outcomes resulting from third-party use of these models.

The model was trained using Jax/Flax and converted to PyTorch, Tensorflow, whisper.cpp, and ONXX formats. These are available under Files and versions . We welcome requests for conversion to other formats. All training code and scripts are released under the Apache License 2.0 in the GitHub repository nb-whisper .

Citation & Contributors

The NB-Whisper Base Verbatim model is a product of the NoSTram project led by Per Egil Kummervold ( @pere ) at the National Library of Norway. Key contributors include Javier de la Rosa ( @versae ), Freddy Wetjen ( @freddyw ), and Rolv-Arild Braaten ( @Rolv-Arild ). NB AI-Lab, under the direction of Svein Arne Brygfjeld ( @Brygfjeld ), supported the project's successful completion. A detailed paper on our process and findings is forthcoming.

The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions. When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their use and, in any event, to comply with applicable regulations, including regulations regarding the use of artificial intelligence. In no event shall the owner of the models (The National Library of Norway) be liable for any results arising from the use made by third parties of these models.

Acknowledgements

Our gratitude extends to Google TPU Research Cloud for training resources, Google Cloud for translation credits, and HuggingFace's Sanchit Ghandi for technical support. A special thank you to Per Erik Solberg at Språkbanken for the collaboration on the Stortinget corpus.

For feedback, technical concerns, or collaboration inquiries, please contact [email protected] . If you plan to include this model in your research, contact us for the latest information on our upcoming paper for citation purposes.

Finetuned from openai/whisper-base

Datasets used to train nbailab/nb-whisper-base-verbatim, collection including nbailab/nb-whisper-base-verbatim, nb-whisper-verbatim.

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Freshman Democrats Work to Turn Biden Impeachment Effort on Its Head

A crop of novice lawmakers on the House Oversight Committee has countered Republicans’ allegations against President Biden with attention-grabbing charges of their own.

By Luke Broadwater

Reporting from the Capitol

Representative Jasmine Crockett was sitting in a House Oversight Committee hearing last fall, growing increasingly frustrated as she listened to Republicans accuse President Biden of impeachable offenses without producing any evidence, when she had an idea.

Ms. Crockett, a freshman Democrat from Texas and former defense attorney, summoned an aide and asked them to quickly print out a stack of photos showing the boxes of sensitive government documents stashed by a toilet at Mar-a-Lago, former President Donald J. Trump’s club in Palm Beach, Fla.

Moments later, Ms. Crockett was brandishing the photos above her head, accusing Republicans of ignoring clear evidence that Mr. Trump had violated the law while pushing allegations against Mr. Biden for which they had shown no proof.

“When we start talking about things that look like evidence, they want to act like they blind,” Ms. Crockett said of Republicans, spitting her words with a mix of outrage and bemusement. “These are our national secrets,” apparently in a toilet, she added, using an expletive to describe the plumbing.

The moment circulated widely on social media. The White House took notice. So did senior House Democrats. Suddenly, it was Ms. Crockett, not the Republicans pursuing Mr. Biden, who was capturing the public’s attention.

The performance has become something of a hallmark of the sputtering Republican effort to impeach Mr. Biden, which has faltered in recent weeks as the G.O.P. has come up empty in its efforts to back up its claims of wrongdoing by the president.

As the Republicans have pressed their case against Mr. Biden, Democrats on the Oversight panel — including an unusually large crop of freshman — have matched them sound bite for sound bite and stunt for political stunt, establishing themselves as feisty defenders of the president.

It’s a strategy that Democrats began planning out more than a year ago. Back in January 2023, they selected seven freshmen to sit on the Oversight panel, the most of any committee. The group included lawyers with debate experience and members who had a sense for how to communicate in a way that could catch fire on social media and break through the noise of a highly polarized environment.

The result has been that the impeachment proceedings that were designed by Republicans to damage Mr. Biden politically have instead elevated the profiles of a group of battle-ready first-term Democrats who have become fixtures of the partisan scrum that is the House Oversight Committee.

In addition to Ms. Crockett, there is Representative Dan Goldman, a former federal prosecutor from New York, who has made it his mission to beat Republicans to the microphones outside of closed-door interviews, framing the testimony before his G.O.P. rivals can.

Representative Robert Garcia of California has peppered his remarks with sassy pop culture references that have gained traction on social media, drawing attention to the Democrats’ defense.

And Representative Jared Moskowitz of Florida has gained a reputation as the chief antagonist of Representative James R. Comer, who as chairman of the committee is leading the investigation. Mr. Moskowitz has repeatedly gotten under Mr. Comer’s skin with irreverent tactics, including once wearing a mask of President Vladimir V. Putin to a hearing to mock him as a puppet of Russia.

Even without a concerted campaign by Democrats, the Republican drive to impeach Mr. Biden would likely have struggled to gain momentum. Its leaders have never been able to establish the kind of evidence needed to convince mainstream and swing-district members to move forward with impeachment, a critical task given their tiny majority. And their investigation was dealt a near-fatal blow when a key informant was charged with fabricating his story of Mr. Biden accepting bribes.

Many Republicans are now conceding that their push to impeach Mr. Biden is all but dead, and Mr. Comer has pivoted to exploring possible criminal referrals instead , which he has called “the culmination of my investigation.”

Democrats argue their strategy has been critical to derailing the enterprise. They battled Republicans on the facts, sought to shift the focus to Mr. Trump’s misdeeds and — perhaps most importantly — mirrored the G.O.P.’s incendiary tactics.

“I think it’s clear that we out-messaged them, which is why now they’re coming out and admitting that they’re not going to be impeaching Joe Biden,” Mr. Moskowitz said.

They did so under the leadership of Representative Jamie Raskin of Maryland, the top Democrat on the Oversight Committee, and Representative Alexandria Ocasio-Cortez of New York, his No. 2.

Ms. Ocasio-Cortez said it was paramount to counter any Republican narrative before it could take hold. She saw how Republicans built momentum toward impeaching Alejandro N. Mayorkas, the homeland security secretary, and wanted to avoid a similar case developing in the Oversight Committee against Mr. Biden.

“A lot of what we’re doing is calling the plays and figuring out the strategy,” Ms. Ocasio-Cortez said. “What kind of question lines and topics may be best for which members? How do we want to build a crescendo and tell a story over the course of a hearing? How can we work with some of our freshman members for them to cultivate ideas of their own?”

In another era, freshmen like Ms. Crockett would have been unlikely to register on leaders’ radar. But there’s a new model on Capitol Hill, inspired in part by Ms. Ocasio-Cortez herself, who in 2018 at the age of 28 became the youngest woman elected to Congress.

She learned her Oversight skills under Representative Elijah E. Cummings, the Maryland Democrat who chaired the committee and died in 2019. When she had to choose between being on the Financial Services Committee, where lawmakers can raise large campaign donations from Wall Street, and Oversight, Ms. Ocasio-Cortez made a choice previous New York lawmakers would have found unthinkable.

While the Oversight Committee has been known as the home of partisan fighting and grandstanding, Mr. Garcia said Oversight had been his “top choice.”

Mr. Garcia’s moment of social-media fame — planned in advance, by his own admission — came during a January hearing in which he mocked Republicans’ Biden impeachment drive by quoting, nearly verbatim, a famously dramatic and detailed takedown from an episode of the “Real Housewives of Salt Lake City” reality television show.

Democrats, Mr. Garcia said from his seat on the dais on Capitol Hill, “have receipts. Proof. A timeline. Screenshots. We have everything we need to prove conclusively that foreign governments were funneling money through Trump properties and into Donald Trump’s pockets, all in violation of the Constitution.”

Mr. Trump has denied any wrongdoing in his dealings with foreign governments. But the moment had its intended effect. The “Real Housewives” delighted over Mr. Garcia’s remarks and circulated them widely on social media, and the Bravo host Andy Cohen featured them on his popular nightly “Watch What Happens Live” program.

“We need to communicate to the public in ways they can understand and I think that for me, that moment reached so many people that were not tuning in to what’s happening with the Oversight Committee,” Mr. Garcia, the first openly gay immigrant elected to Congress , said in an interview. “It was effective because they understood it in a way that spoke to them.”

Mr. Goldman has taken a different approach. As the lead counsel for the first impeachment inquiry against Mr. Trump, Mr. Goldman knows the evidence on Ukraine and Hunter Biden better than most.

He has made it his business to publicly push back against Republican efforts to twist facts to fit their allegations of wrongdoing by the president and members of his family.

“I knew that the Republicans were going to have closed-door depositions, and then selectively leak parts of those out to try to frame a false narrative. And so I was not going to allow them to do that,” Mr. Goldman said.

It is clear that Mr. Comer has lost patience with the Democrats and their tactics.

He has complained about the freshmen, saying that they intimidate his witnesses. He has called Mr. Moskowitz a “Smurf,” prompting the freshman to dress like one, wearing blue shoes and a Smurf tie to the next hearing.

“I’ve never seen such witness intimidation as what I saw today,” Mr. Comer said on Newsmax in February, singling out Mr. Raskin, Mr. Goldman, Mr. Garcia and Ms. Crockett. “They were wagging their fingers. They were pointing, they were yelling.”

At a recent hearing, Mr. Moskowitz insisted on calling for a vote on impeaching Mr. Biden, knowing full well Republicans lacked the votes. Mr. Comer looked annoyed, and refused to second the motion. But even Representative Jim Jordan, one of the top Republicans leading the impeachment drive, cracked a smile.

Ms. Crockett didn’t ask to sit on the Oversight Committee; she was angling for Financial Services or Judiciary. But Representative Katherine M. Clark, the No. 2 Democrat in the House, persuaded her to join, and it turned out to be a natural fit.

Ms. Crockett is no stranger to partisan battles. She came out of the Texas State House, where, at one point, Republicans issued a warrant for her arrest amid a dispute over Texas voting laws . (The framed warrant now hangs in her Dallas office.)

She makes sure she is camera-ready, ducking into a dedicated makeup room in her office to get ready for her frequent television appearances.

She and other freshmen have found themselves frequently in battle with Republican bomb throwers, such as Representative Marjorie Taylor Greene of Georgia. One of the first assignments Ms. Crockett and Mr. Garcia got was to tour the D.C. jail with Ms. Greene to counter her narrative that the rioters who stormed the Capitol on Jan. 6 were political prisoners being held in inhumane conditions.

Then Ms. Crockett found herself seated near Ms. Greene at a congressional hearing as the Georgia congresswoman displayed naked photos of the president’s son Hunter Biden engaging in sex acts.

“It was one of those ‘We’re frozen’ moments. Like, what do we do?” Ms. Crockett recalled. “We were all looking at each other, like, did that just happen? We were all in shock and awe.”

But Ms. Crockett is almost never at a loss for words. Her committee speeches have repeatedly made headlines in left-leaning outlets.

“With all the viral moments and all of the antics, people assume, ‘Oh she wanted that.’ Actually I did not,” Ms. Crockett said of her assignment on the Oversight Committee. “But it’s worked out for sure.”

Luke Broadwater covers Congress with a focus on congressional investigations. More about Luke Broadwater

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