Contributor: Mariana Grohowski
Affiliation: Massachusetts Maritime Academy
Email: mgrohow at gmail.com
Published: 29 February 2016
When I was a teenager in the nineties, I couldn’t simply Google the lyrics to my favorite song. When I wanted to know a song’s lyrics, I took it upon myself to transcribe the lyrics by hand as I listened along. I spent countless hours struggling to capture a song’s lyrics on paper. I painstakingly tried (and failed) to keep up with the pace of the singer as I scribbled out the words by hand. This process entailed repeatedly pausing and rewinding portions of the song in order to capture the correct lyrics. It was a futile attempt of transcribing audio.
Twenty years later as I was working on my dissertation, I found myself in a position comparable to that of my teenage-self. Though I no longer had the need to transcribe song lyrics by hand, I did have a similar aim to meet: to convert data from one modality into another. I needed to transfer a corpus of audio-recorded, oral history interviews into alphabetic transcriptions for coding. Indeed, this is a common need for digital humanities researchers who utilize interview and oral history as primary research instruments.
From 2013 to 2014, I conducted, digitally recorded, and transcribed over forty-six audio interviews. Interviews took a variety of formats (phone, face-to-face, video conference) and ranged in duration (from twenty minutes to two hours); each interview was digitally recorded using the “Voice Memos” app on my iPhone, or through the Macintosh application GarageBand on my laptop.
Because I interviewed U.S. military veterans—a protected, misrepresented, and misunderstood (Doe & Langstraat, 2014; Pew, 2011) population in U.S. society—I felt an “ethical obligation” (Hart & Thompson, 2013; Valentino, 2010) to capture, as closely as I could, the integrity and uniqueness of interviewees' voices. Though I had heard about other researchers paying money for a transcription service or speeding up the tiresome process of transcription by completing “selective transcriptions,” these options would not allow for the “deepened understanding of participants' cultures and worldviews” I was seeking. What I sought to cultivate through my transcriptions (and later coding) was explained by Anselm Strauss (1987) as “In Vivo." According to Strauss, "in vivo" transcription and coding processes account for “the terms used by participants themselves" (p. 33) (For more information on "in vivo" see also Charmaz, 2006; Saldaña, 2013).
For the sake of honoring participants’ voices, it was imperative for me to perform close listenings of our audio-recorded interviews. Additionally, my methodology required me to find a way to capture accents, pauses, and sighs amidst interviewees' rich perspectives and detailed experiences. Admittedly, the magnitude of my task was intimidating in light of the fact that I assumed my best method for transcription was that which I had utilized as a teen scribbling song lyrics. Imagine my relief when I discovered the transcription application transcribe.wreally.com.
A Web application, transcribe.wreally was designed exclusively for the Web browser Google Chrome. As characteristic of a Web app's design, transcribe.wreally does not mandate a need to download or learn a new software to transcribe audio files. Instead, transcribe.wreally affords the digital humanities researcher an efficient audio transcription process for data collection. The app is an extension of the Chrome Web browser. Users can transcribe their audio files on- and offline via a Chrome Web browser window and a standard computer keyboard. The Web app enables the user to use the Escape and function keys (F1-F4) on a computer keyboard to:
- slow down the audio (F1);
- speed up the audio (F2);
- rewind the audio by 2 seconds (F3);
- move forward in the audio by 2 seconds (F4);
- and to pause the audio (Esc).
The affordances of the app are its design and ease of use, which I describe and illustrate below. Because the app reduces a researcher’s distraction by the transcription tools, the app promotes active listening and increases accurate transcription. By describing how this app informed my research process, I demonstrate its usefulness within digital humanities research, especially such work that incorporates oral history or interview as a primary data collection instrument.
Using screen capture video with audio, the following two videos and accompanying transcriptions illustrate three key steps in a new user’s experience. Video 1 shows the user setting up an account and engaging in the tour of the app’s features. In Video 2, the user walks through the process of uploading an audio file and utilizing the app’s features to transcribe.
Though setting up accounts has become commonplace through digital and social media platforms, setting up an account on transcribe.wreally also informs first-time users of the complimentary, seven day trial of the app, and the option to purchase an account for a year's rate of twenty dollars.
New users receive a brief tour of the app’s main features, facilitated by nine pop-up boxes. Such features demonstrate the usefulness of the app, as well as how quickly it can be learned and used for audio data transcription. The tour ends on the app’s main screen—the exclusive location for generating and storing an audio file and its respective transcription, as shown in the final screencast.
The final screencast also briefly demonstrates the process of uploading an audio file from one’s computer into the transcribe.wreally web app.
My own experience using this application further demonstrates its capabilities. I used the transcribe.wreally Web app exclusively to transcribe the more than forty-six audio-recorded interviews. After recording interviews through the Macintosh application GarageBand, I exported the audio files to iTunes. This process afforded me a means for organizing and storing the audio files, labeling and organizing them via “playlist” based on the interviewee and/or research study. Using iTunes to store audio files facilitated a seamless retrieval process when using transcribe.wreally (as illustrated in video 2). Use of the keyboard not only to generate the alphabetic transcription but also to pause (Escape key), slow down (F1 key), or rewind (F3 key) the audio made transcribing less difficult. Indeed, keeping my fingers on the keyboard rather than moving my entire hand to the mouse or trackpad sped up the transcription process and decreased the potential for error. Because I could pause and rewind the audio through the function keys on my keyboard, I felt more confident in my transcriptions; I felt that I was capturing the integrity of participants' unique phrasings and terms. Additionally, the ability to transcribe and manipulate the audio file in a single location decreased my time and effort. Given the design of the Web app, the data (audio file and alphabetic text generated by the transcriptionist) is stored in the Chrome browser window—the app’s interface.
I assume other digital researchers who compose transcriptions may be as skeptical as I was in trusting that the browser window would save my content. Though I never lost any of the text I generated in the browser window while transcribing, I always played it safe. During transcription sessions lasting multiple hours, I briefly paused the act of transcription in order to copy and paste the text I had generated into a Microsoft Word or Google Doc as a backup. Because I used the transcribe.wreally app so much during my research study, the twenty-dollar yearly subscription was reasonable. Furthermore, the return on my investment was substantial: not only did the app allow me to capture participants’ voices and perceptions “In Vivo” (Charmaz, 2006; Saldana, 2013), it also inspired ideas for future research studies.
Transcribe.wreally.com facilitated a means of active listening by allowing me to focus deeply on participants’ language use. Because I was not distracted by the transcription technology, I could concentrate on the data. By focusing squarely on the audio data and the processes of alphabetic transcription, I noticed the effect my language choices had on interviewees. The app helped me to discover my faults as a listener during interviews, which subsequently inspired me to be a better listener during transcription. It was the features of the app—the ability to pause, slow down, and rewind the audio so seamlessly—that not only helped me to practice listening but also influenced (for the better) the data and knowledge I produced.
Of course, the app has its imperfections, many of which the user is forced to navigate at the outset. Here are several limitations of the app:
- The app can only be used through the Web Browser Google Chrome
- The free trial period is a mere seven days, which is disappointing, especially in comparison with other platforms like Adobe PDF or Camtasia, which have 30 day trial periods
- To continue use after the seven day trial period, the user must pay a yearly fee
- Mac users will need to enable the use of keyboard function keys (a main feature of the app is its use of function keys F1-4) in "system preferences" (Click for step-by-step directions)
- As seen in video 2, immediately upon upload of the audio file within the app's interface, the audio file begins playing
Beyond these limitations, transcribe.wreally affords digital humanities researchers ease and control over qualitative data transcription and presents exciting implications.
While my work consisted of interviews and oral histories, the app can also be utilized to dictate spoken word into alphabetic text. As shown in the tour of the app in Video 1, the app has a dictation feature. The dictation feature might be helpful for ethnographic researchers who seek to create post-facto notes and/or memos but are pressed for time or prefer talking out ideas to written reflections. The app’s design streamlines and enhances the digital researcher’s processes for transcription or dictation in two important ways:
- The app increases researcher’s success, thereby reducing the margins for human error, during the taxing process of manual transcription.
- The app allows the digital humanities researcher to do such work affordably, eliminating the need for expensive transcription software or tools that many digital humanities researchers do not posses.
Transcribe.wreally increases accessibility for data analysis. Transcription and data analysis can present many barriers to the researcher such as time, cost, access to technologies or assistance for transcription, a tool’s difficulty of use, or a user’s level of ability to use a specific tool or resource. The tools we use as researchers should work with us and for us rather than against us. As digital researchers know, there are many digital tools that respond to our particular needs, methods, and methodologies. For me, transcribe.wreally met my methodological aim to produce “In Vivo” transcriptions. Word for word, or “In Vivo” transcriptions were important for the purposes of my research, given my research population of U.S. military veterans. Through the process of transcription with the transcribe.wreally web app, I was made aware of the level of detail in the oral data that I could access, cultivate, and further explore. The pace and ease of being able to slow down and pause the audio revealed details I had not noticed—and perhaps would not have considered—during interviews.
For most researchers, an important aim is increased engagement with data. The Web app transcribe.wreally allows digital humanities researchers the ability to enhance their engagement with data in an accessible, efficient, and time-sensitive manner. Understanding how digital data can be manipulated via software like transcribe.wreally not only affords the researcher more control of his/her data, but also increases his/her engagement with data in unique and exciting ways.
Charmaz, Kathy. (2006). Constructing grounded theory: A practical guide through qualitative analysis. Los Angeles, CA: Sage Publications.
Hart, D. Alexis, & Thompson, Roger. (2013). 'An ethical obligation': Promising practices for student veterans in college writing classrooms. Urbana, IL: National Council of Teachers of English. PDF file.
Klay, Phil. (2014, Feb. 8). After war, a failure of the imagination. The New York Times. Retrieved from http://www.nytimes.com/2014/02/09/opinion/sunday/after-war-a-failure-of-the-imagination.html?_r=0
Pew Research Center. (2011a). War and sacrifice in the post-9/11 era. Retrieved from http://www.pewsocialtrends.org/2011/10/05/war-and-sacrifice-in-the-post-911-era/
Saldaña, Johnny. (2013). The coding manual for qualitative researchers. Los Angeles, CA: Sage Publications.
Strauss, Anselm L. (1987). Qualitative analysis for social scientists. Cambridge, UK: Cambridge University Press.
Valentino, Marilyn. (2010). 2010 CCCC chair's address: Rethinking the fourth c: Call to action. College Composition and Communication, 62(2), 364–78.