Transcription Tools

The Social Science Research Center at DePaul has a micro-lab where researchers (or their graduate students) can access hardware and software to transcribe audio files.  Typically, researchers have used these tools to transcribe interviews and focus groups.  The process is relatively simple: researchers bring their audio files on portable media, which are loaded onto a machine in the micro lab.  This machine has a software called “Express Scribe” and a pedal.  The pedal is used to stop, start, rewind and fast forward the audio within the environment of Express Scribe.  Additionally, the speed of the audio playback can be modified.  In all, this is a great tool and process for individuals to transcribe audio files.  However, it is not without its flaws.  The main flaw is that it requires users to be in the physical space during business hours.  Also, it requires that someone spend the time actually typing the text of the transcription.

In this post, I review two relatively new transcription tools and demonstrate how they might be used to help researchers transcribe spoken language.

The first, oTranscribe is a web-based transcription tool.  With it, you upload an audio file and from within the web page, you control audio playback.  Keep in mind that if a researcher were going to do this on their own (without coming to the SSRC to use our machine and pedal), this would require playing the audio in something like iTunes and typing the text in a text editor (like MS Word).  Which is likely fine, if you’re working on a machine with two monitors.  Even so, stopping and restarting the audio file can be quite cumbersome using this approach- even if you are capable and have figured out how to use hotkeys and shortcuts.  Remember that hotkeys usually require that you be in the program to use it.  So, you’re typing in MS Word, but in order to get audio to stop you have to get back to iTunes with the mouse and actually press stop (or click in the window with iTunes and use a hotkey to stop the audio file).

oTranscribe allows you to do this all in the same place.  Even better, when audio is restarted, it repeats the the last bit of where you left off.  This gives you a chance to get your hands in place and allows for a much easier orientation.  In the default setup, the key to stop and start the audio is the ESC, but you could change that.  Additionally, the audio can be slowed down quite a lot.  I have demonstrated what the process is like here.

I recorded myself reading the beginning of a chapter in Howard Becker’s Writing for Social Scientists on an iPhone (using the Voice Memos app).  Although it sounds like I might be drunk, I am actually not.  I have slowed the audio down enough so that I can keep up typing it.

Overall, not a terribly onerous process.  I think it beats having to toggle back and forth between different programs.

I learned about Scribe, a tool that does automatic transcription.  According to Poynter, it was developed by some students working on a school project.  One of the students had to transcribe 12 interviews, and he didn’t want to do it (who does?).  He built a script that uses the Google Speech API to transcribe the speech to text.  Based in Europe, the Scribe website asks that a user upload an mp3 and provide an email address.  The cost to have the file transcribed is €0.09 cents per minute.  As of now, there is a limit to how long the audio file can be (80 minutes).  Because the file format from the Voice Memos app is mpeg-4, I actually had to convert my audio file before it could be uploaded.  Once this was done, I received an email with a link to my text when the transcription was finished.

Below is the unedited output that I received.  I pasted the text into OneNote so that I could add highlighting and comments.

scribe_edit

In all, I am fairly impressed with the output from Scribe.  Obviously, there are some problems with it.  The text is generally right- organized in paragraphs, but not naturally.  For example, the second paragraph is separated from the first, when they should have been kept together.  There were periods at the end of the paragraphs.   Also there is some random capitalization (i.e. “The Chronic”). Amazingly, names were capitalized (Kelly and Merten), which I thought was remarkable.  My guess is that the mix-ups with chutzpah/hot spot and vaudeville/the wave auto are fairly common with words borrowed from other languages.

Obviously, the text will need a little work.  While I think Scribe works well for interviews, I am not sure how well it would work for focus groups.  Of course, the text needs some review and editing, but I think that in the long run it would be faster to correct mistakes than it is to manually type the transcription.  The kicker for me, is how cheap it is: at €0.09 cents per minute, an 80 minute interview could be transcribed for less than $10.00.

I think that both oTranscribe and Scribe lowers the bar to entry for researchers wanting to transcribe audio material.

Learning How We Write with Sarah Read

During a research leave this past Spring Quarter, Assistant Professor Sarah Read of Writing, Rhetoric & Discourse (WRD) has been utilizing ATLAS.ti in the SSRC’s computer lab for her ethnographic project at the facility that operates the world’s fifth largest supercomputer.  With visiting privileges from Argonne National Laboratory as a guest faculty researcher, Sarah is analyzing the technical documentation and reporting processes of the Argonne Leadership Computing Center where the cutting-edge machine is housed. She is studying technical documentation and reporting processes with a focus on the daily activities of the “knowledge workers” who operate it. Her focus is on writing, traced through documentation related to and generated by the supercomputing center.

SarahReed_ssrcLab_HiDef

“I think it’s fascinating to create an account of what it takes to build and operate a world-class supercomputer,” Sarah said. Through her interviews with supercomputing center staff, Sarah has been delighted to discover that they “think like researchers” themselves, tackling uncharted terrain in what is “essentially a research project in supercomputing.” Every year the staff prepares a report on operations required by their sponsoring federal agency. In this report they demonstrate how the facility has met the metrics of availability, utilization and capability supported by data that staff must work out how to generate. “It’s a big machine. It’s not easy,” she notes. Nothing is pre-formed. Staff have no manual to consult when the machine fails, no button to press to spit out the right data. Figuring out how to “write down the machine” is a complex, research-based task itself.

A theory-driven researcher and ethnographer, Sarah describes herself as “fundamentally a rhetorician, but I study it in technical environments.” The project combines her competing interests and background in writing, science and the humanities. “I consider myself a collector of qualitative data, but I don’t consider myself really a social scientist. I’m kind of in a grey area epistemologically,” she explains. Her ethnographic approach, which interests her in “the theory-driven points of view,” permits her to admit to strong biases. “I think there’s value in creating descriptive accounts of phenomenon within new theoretical frameworks that make visible previously invisible aspects of that phenomenon” she said. “It makes the strange mundane and the mundane strange again.”

As a self-taught, new fan of ATLAS.ti, Sarah too is learning as she progresses, experimenting with how to make the application work within her methodologies. This spring she worked hard with the networking view tool and is now concentrating on coding. ATLAS is “very object-oriented,” she said, treating chunks of a transcript as a whole document. She’s impressed with its analytical power and its visual capabilities that helpfully reveal networked relationships among research artifacts. “It’s a tool for analysis, but network views can also be research products” she noted.

Currently Sarah is continuing to code interview data for an article about how gathering data for the operations report structures staff work activities at the supercomputing facility. She is also writing a proposal for a book about the infrastructural function of writing and documentation for technical organizations.

Sarah offers to share what she’s learned about ATLAS.ti with other DePaul researchers. She’s found that talking through the process is mutually beneficial. If you’re interested in learning more about the application, please contact the SSRC[ssrc@depaul.edu] or Sarah [sread@depaul.edu].

Browser-based qualitative data analysis – dedoose

Mac and Linux-based qualitative researchers have a potential new tool available to them in dedoose, a multiplatform browser-based system for coding and analyzing qualitative data.  It makes dealing with data on any platform pretty straightforward, and was built for big teams. All data and analysis occur online, making projects easily accessible to many users working from nearly anywhere with an internet connection.

That said, my initial experiences with dedoose (a name that makes me cringe whenever I have to type it), weren’t great. One reason I love ATLAS.ti for qualitative analysis is that it handles a broad range of file types; specifically, it allows you to code directly onto pdfs without a hitch. Dedoose wasn’t too crazy about loading the pdfs of 1960s newspaper articles that I’m working with. As a historical sociologist, this is pretty crucial for my work, though it’s much less important for most qualitative researchers. Additionally, the dedoose web app crashed when I tried to load Word .doc and .docx files. While it’s easy to convert these into .rtf or plain text files, it’s a frustration, especially considering dedoose’s claim that it supports these file types natively. Its user interface also feels a bit stiff and outdated, though aesthetics admittedly are never the first thing you look for in research software. Continue reading “Browser-based qualitative data analysis – dedoose”

Certificate in Qualitative Research Methods: Summer Session

After a successful first run, the SSRC will again offer a certificate course in qualitative research methods. The course will run on Saturdays from June 2 – July 7 (with no class on June 30).

This course will teach students the fundamentals of scientific qualitative research design and how to conduct the most common types of qualitative field research, including in-depth interviews, ethnography, life narratives, focus groups, and participant observation. Students will get hands-on, practical experience designing and conducting qualitative research, including data collection techniques. By its conclusion, they’ll be able to:

  • Develop and elucidate testable hypotheses
  • Understand how social theory and specific research methods work together
  • Recognize an appropriate methodology based on research questions and develop a corresponding research design
  • Craft quality control mechanisms for data collection activities
  • Design data collection instruments
  • Enumerate various ethical and political dilemmas in qualitative research
  • Conduct qualitative field research
  • Identify the most common qualitative field research pitfalls and strategies to avoid them

Chicago-area professionals working within behavioral and/or social science research and those just wanting to bolster their marketable research skills will benefit from the certificate program.

Recent graduates, both graduate and undergraduate, current graduate students, and advanced upperclassmen considering a career in social or behavioral science fields or graduate programs in sociology, psychology, anthropology, public service, policy studies, social work, and public health would also benefit.

Registration is now open! Contact Jessica Speer (jspeer3 at depaul.edu) to learn more.