MAXQDA Research Blog
April 2019
1
MAXQDA
Research Example
AI Transcription Services
How to Use AI Transcription Services with MAXQDA
Matthew Loxton
Abstract: The advent of machine learning represents a dramatic change in the effectiveness and efficiency
of voice-to-text software. The same machine-learning technology can be used to transcribe interviews. I
reviewed 3 AI transcription services and used one of them throughout a current MAXQDA 2018 project.
Keywords: AI, Automatic speaker change, Computer Assisted Qualitative Analysis, functions, interview
data, Machine Learning Technology, Multimedia Browser, transcribe interviews, voice-to-text
Table of Contents
1. Introduction
2. How MAXQDA’s Transcription Mode Feature Can Be Used in Conjunction with Machine Learning
Voice-to-Text Services
3. Recording and importing the interviews
4. Method 1 – Microsoft Word
5. Method 2 – SRT File
6. Conclusion
Editor´s Note
1. Introduction
For some people, such as myself, MAXQDA’s Transcription Mode tools are a wonderful help. The
software is not, however, able to make us efficient in transcribing audio files. Even with the many
functions available in MAXQDA’s Multimedia Browser, such as keyboard shortcuts and automatic
speaker changes, I am still far too clumsy with hitting F4, the pause button, or the various other
buttons, to transcribe the audio efficiently.
My typing is too slow, my working-memory too short, and I get confused between the function
keys and buttons. As a result, transcribing a 30-minute audio file takes me several hours, and by
the end, I am exhausted.
2. How MAXQDA’s Transcription Mode Feature Can Be Used in Conjunction
with Machine Learning Voice-to-Text Services
The advent of machine learning represents a dramatic change in the effectiveness and efficiency
of voice-to-text software. Where previously, voice-activated systems and voice-to-text applications
were stuck at low levels of accuracy and speed, the use of machine learning has resulted in a
variety of devices and services that boast of 95% and higher accuracy.
So much so, that Siri, Alexa, Cortana, and Google Home are now part of life for millions of people.
Google Duplex takes this further and is almost indistinguishable from a human assistant.
Transcribing interviews
MAXQDA Research Blog
April 2019
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MAXQDA
Research Example
AI Transcription Services
The same machine-learning technology can be used to transcribe interviews. I reviewed three
different options and services, ultimately using one of them throughout a current project.
My aim was to speed up my transcription process, and to remove the frustration that normally
accompanies this step in the research process for me. I recorded nine interviews, which ranged
from 18-48 minutes, and in which there was one participant, the primary interviewer, and myself.
Three voice to text services
Based on some research on popular services, the three services that I looked at were:
I found that approximately 1-2% of the participant’s text from the two Machine transcriptions
needed edits, typically for similar-sounding words such as “IV” vs “IB”, or “wake” vs “wait”. I
also needed to change the speaker names manually. I had higher error rates in the interviewer
text, but this was not of significance to my needs.
The low cost and almost immediate turnaround of the machine transcriptions meant that for just
over $20 in total, I could have very workable transcriptions almost ready to code within minutes
for nine of the half-hour interviews.
I found that making the few edits required was far less frustrating and time-consuming than
manual transcription had been in the past. This is obviously a function of my typing speed and
keyboard dexterity, but may be similar for many other researchers.
3. Recording and importing the interviews
I used Skype for Business to create the meeting requests for the interviews, provide Voice over IP
(VoIP) services and dial-in numbers, and allow me to record the call as an MP4 file with good
audio quality. I experimented with two alternative processes:
In the first, I used the content of an MS Word document from the transcription provider, and in
the second process, I used MAXQDA’s Transcripts with Timestamps function to import a SubRip
(SRT) file with timestamps from the provider.
My transcription environment, captured in Figure 1, shows text from the machine learning service
pasted into MAXQDA’s “Document Browser” window for the selected audio file imported into
the Document System (highlighted in the “Document System” window).
At this point, I am about to edit the name of the second speaker using the Automatic Speaker
Change and Autotext functions:
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April 2019
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Research Example
AI Transcription Services
4. Method 1 – Microsoft Word
My process using the MS Word file was as follows:
1. Download the transcription from the online service as an MS Word document,
2. Import the MP4 into MAXQDA,
3. Open the file for transcription,
4. Paste the content of the Word file into the Transcription Mode “Document Browser”
window,
5. Run the audio in MAXQDA’s Multimedia Browser, and make any edits in the text as
needed,
6. Add timestamps at each speaker change or where desired.
Method 1 Findings
The method was effective, and I will continue to use this in production because it has greatly
reduced the time and effort to transcribe audio tracks at very low cost. There were some caveats,
however.
Speaker changes
Firstly, the machine transcription did a reasonably good job of speaker changes, but sometimes it
got confused. Sometimes it thought there was a different speaker, when in reality, it was the
same person continuing to speak (Figure 2).
This was not a major hurdle, and the changeovers were reasonably obvious, and mistakes were
easily corrected. Sometimes it changed over to a new speaker a little late, and switched speakers
several words into the next speaker’s text. In Figure 3 the highlighted text belongs to Speaker 1,
not to the previous speaker. This too was reasonably easy to spot, and to edit.
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April 2019
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Research Example
AI Transcription Services
Multiple speakers
Secondly, it did not like multiple people talking at the same time. When there were multiple
interleaved or simultaneous speakers, it tended to think it was the same speaker, and also
sometimes lost several words of each speaker.
This issue can be seen in the first paragraph in Figure 1 (above) where the participant and
interviewer start the interview talking about whether the VoIP system had announced that it
was recording. Both speakers were captured as a single person, and their phrases intermingled.
This was harder to identify, and to edit but tended to happen at specific points in the interview,
such as when there was a transition in topic. However, there were at least one or two
occurrences in each interview, especially when something startling or funny was said, and
multiple people interjected.
For example, when one participant commented that they had been left alone in the MRI room
and staff had left for lunch, both interviewers interjected with comments, laughter, whistles,
etc. The machine transcription missed some of the words, and mingled them together in small
phrases. This was less easy to edit, but not any worse than was the case for me doing it
manually.
Lower volumes
Lastly, it often ignored audio that was significantly lower volume than the rest (it may have
calculated an average volume and then filtered out quieter sounds as noise). One recurring
situation brought this to light. At the end of each participant track, the interviewer typically
thanked them in a much quieter voice and then used normal volume to initiate the next question
or comment.
In many cases, these “asides” were not transcribed at all. This was not a problem for my situation,
but it may be a significant issue if the participant varies in volume.
5. Method 2 – SRT File
My method for the SRT file used MAXQDA’s Import Transcripts with Timestamps function:
1. Download the SRT file,
2. Import the SRT into MAXQDA using the Import Transcripts with Timestamps
function,
3. Point to the associated MP4 file in the resulting dialogue box,
4. Open the file for transcription,
5. Run the audio in MAXQDA’s Multimedia Browser,
6. Make edits in the text as needed.
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Research Example
AI Transcription Services
Method 2 Findings
The accuracy rate was the same, and the same cost and logistics considerations applied. The
caveats were also similar, and although importing the SRT had an advantage in not requiring
me to add timestamps manually, it resulted in a highly fragmented text layout inherited from
the SRT file structure.
While the audio and text tracked well together, viewing the text as a vertical list of fragments
was difficult to read, and coding was made significantly more difficult. As a result, I did not
continue with the use of the SRT file method after the initial test.
6. Conclusion
The low cost and short turnaround time make machine-learning transcription services worthwhile
to some researchers whose keyboard dexterity makes manual transcription tedious. The services
do not offer perfect transcription, however, and each method to use them comes with some
caveats.
Using the MS Word import was found to be preferable to using the SRT method, and what was
lost in the more comprehensive timestamping was gained in the greater readability of the
resulting text of the Word import.
My overall finding was that both TEMI and SPEXT were good enough to continue to use, but that
if high fidelity was needed and cost and time were less of an issue, the human-machine
combination would be an attractive option.
Editor´s Note
Matthew Loxton is a Principal Analyst at Whitney, Bradley, and Brown Inc. focused on healthcare
improvement, serves on the board of directors of the Blue Faery Liver Cancer Association, and
holds a master’s degree in KM from the University of Canberra. Matthew is the founder of the
Monitoring & Evaluation, Quality Assurance, and Process Improvement (MEQAPI) organization,
and regularly blogs for Physician’s Weekly. Matthew is active on social media related to healthcare
improvement and hosts the weekly #MEQAPI chat.
