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Methodological Innovations
May-August 2018: 1–14
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DOI: 10.1177/2059799118790743
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Introduction
A number of key methods in social science involve some
form of transcription of audio. As the UK data archive posits,
transcription work is a time-consuming process that often is
outsourced to external transcribers (UK data archive, 2017).
In projects involving multiple rounds of data analysis, it is
important to follow standardised guidelines while transcrib-
ing. This is one of the reasons why large-scale qualitative
projects often release a separate detailed transcription and
translation manual. There are various types of transcript of
audio recordings for research purposes, depending on the
degree of detail required in the transcription process, from
capturing additional information such as pauses and intona-
tion, through to the production of condensed or essence tran-
scripts where some of the information captured in the raw
audio recording is deliberately omitted from the transcript.
Regardless of the type of transcript required, it is commonly
accepted that the process of transcription is likely to require
multiple rounds of engagement with the audio file (Paulus
et al., 2013). We posit therefore that the generation of tran-
scripts, utilising technology such as automated speech recog-
nition (ASR) tools embedded in web-based auto-captioning
Automated generation of ‘good enough’
transcripts as a first step to transcription
of audio-recorded data
Christian Bokhove and Christopher Downey
Abstract
In the last decade, automated captioning services have appeared in mainstream technology use. Until now, the focus of these
services have been on the technical aspects, supporting pupils with special educational needs and supporting teaching and
learning of second language students. Only limited explorations have been attempted regarding its use for research purposes:
transcription of audio recordings. This article presents a proof-of-concept exploration utilising three examples of automated
transcription of audio recordings from different contexts; an interview, a public hearing and a classroom setting, and compares
them against ‘manual’ transcription techniques in each case. It begins with an overview of literature on automated captioning
and the use of voice recognition tools for the purposes of transcription. An account is provided of the specific processes
and tools used for the generation of the automated captions followed by some basic processing of the captions to produce
automated transcripts. Originality checking software was used to determine a percentage match between the automated
transcript and a manual version as a basic measure of the potential usability of each of the automated transcripts. Some
analysis of the more common and persistent mismatches observed between automated and manual transcripts is provided,
revealing that the majority of mismatches would be easily identified and rectified in a review and edit of the automated
transcript. Finally, some of the challenges and limitations of the approach are considered. These limitations notwithstanding,
we conclude that this form of automated transcription provides ‘good enough’ transcription for first versions of transcripts.
The time and cost advantages of this could be considerable, even for the production of summary or gisted transcripts.
Keywords
Interviews, transcription, qualitative data, automated captions, technology, automatic speech recognition
Southampton Education School, University of Southampton,
Southampton, UK
Corresponding author:
Christian Bokhove, Southampton Education School, University of
Southampton, Highfield, Southampton SO17 1BJ, UK.
Email: C.Bokhove@soton.ac.uk
790743MIO0010.1177/2059799118790743Methodological InnovationsBokhove and Downey
research-article2018
Original Article
2 Methodological Innovations
services, could provide a useful first draft for use in later
cycles of the process of transcription, provided that the qual-
ity of the automated transcript is sufficient to serve as a foun-
dation for further editing, addition and improvement.
This article, therefore, revisits the recommendation from
the UK data archive (2017) not to automate the transcription
of recorded interviews, as these tools ‘all require a great deal
of training and calibration to be able to recognise a particular
voice, accent and dialect’. It also considers whether recent
advances in technology might allow us to utilise freely avail-
able web-tools to quickly come to ‘good enough’ first drafts
of transcripts. We contend that such a workflow might sig-
nificantly reduce the time and costs involved in the transcrip-
tion process. This may well lead to significant gains for
researchers working in fields for which gaining grant fund-
ing for research projects can be particularly challenging.
This may include gains for those researching within contro-
versial fields for which access to external research funding
may be very limited. It should also reduce the overall costs
associated with transcription, and so help avoid the need to
compromise on the scale and scope of a project during the
research design stage, if this is in an effort to balance the
costs of the research within the limits of available funding.
The article engages with a number of relevant themes
from the literature, giving an overview of some of the chal-
lenges in the transcription process from the point of view
of a researcher. The review also considers literature related
to some of the technical and educational aspects of the use
of automated captioning, as well as the use of voice and
speech recognition tools for the purposes of transcription
in a variety of contexts including non-research related set-
tings. This is followed by a proof-of-concept exploration
of an approach to the use of auto-captioning technologies
to generate automated transcripts. Three different audio
sources are used as raw data; captured from three different
environments. One is from a school classroom setting, one
from a public hearing with multiple speakers in a larger
space, and finally audio data from a one-to-one interview
setting. Full details of the process, via freely available
web-based auto-captioning and basic caption-processing
tools, is provided as an example of an ASR approach that
might be used to generate automated transcripts. The qual-
ity of the resulting transcripts is tested through calculation
of a percentage match between the automated transcript
and a manually produced transcript using a common soft-
ware tool for originality checking (Turnitin, 2017). An
analysis of some of the most common and persistent mis-
matches observed between the automated and manual tran-
scripts is provided to consider whether these present
obstacles in the production of automated transcripts that
diminish the utility of the transcripts as a ‘first draft’ effort.
Finally, the challenges and limitations of the approach, via
auto-captioning software tools, are considered before con-
clusions are drawn as to the potential of the technique and
whether the proof-of-concept was successful. The ethical
aspects, for example, are key to consider.
Relevant literature
The theme of this article covers two main areas of methods
research associated with preparation of transcripts from
audio recordings. First, the process of transcription itself,
second, the role of automated production of transcripts. This
section aims to present a non-exhaustive overview of some
of the aspects involved in producing automated transcripts.
Transcribing interviews
Like the UK data archive (2017), research method textbooks
usually describe transcription as a time-consuming process,
forming part of the qualitative research realm (Cohen et al.,
2007). As interviews can be immensely rich in data and detail,
verbatim transcripts are considered to convey these meanings
best (Cohen et al., 2007: 462). The requirement to produce ver-
batim transcripts can also be seen as an act of respect for the
participants in a study: they are devoting valuable time to the
research, and therefore, it is only reasonable to record every
word. However, the time-consuming nature of the transcription
process has caused others to refer to the ‘fetish of transcription’
(Walford, 2001: 92). With a suggested ratio of five to one – 5
hours to transcribe 1 hour of interviews, Walford (2001) sug-
gested it is particularly costly in terms of time. Punch and
Oancea (2014) subscribe to a rule of thumb of needing at least
4 hours for every one hour. Audio recordings are heavily con-
textualised, which means that transcripts ‘inevitably lose data
from the original encounter’ (Punch and Oancea, 2014: 367),
requiring translation from one set of rule systems (oral and
interpersonal) to another rule system (written). Kvale (1996:
166) highlights this by pointing out that the prefix trans in tran-
script indicates a change of state or form, in essence a selective
transformation. Lee (1993) refers to the issue of ‘transcriber
selectivity’, while Kvale (1996:) holds the view that ‘tran-
scripts of interviews, however detailed and full they might be,
remain selective, since they are interpretations of social situa-
tions’ (p. 163). In this view, transcripts might be considered to
be already interpreted data; the transcript acts as a ‘screen’
between the researcher and the original situation of the record-
ing (Kvale, 1996: 167). The ultimate consequence of this is that
there can be no single ‘correct’ transcript, rather only transcripts
that are more or less useful for the research. Taking this relative
notion of transcripts even further, transcripts can be said to be
‘decontextualized, abstracted from time and space, from the
dynamics of the situation, from the live form, and from the
social, interactive, dynamic and fluid dimensions of their
source; they are frozen’ (Kvale, 1996: 367).
Taking both aspects at face value, namely the time-
consuming nature of transcription, and ‘transcriber selectivity’,
we propose that an automated transcription procedure might (a)
save a lot of time and (b) be more detached from the context.
Of course, the latter point is potentially the more contentious.
On the one hand, a ‘dumb’ algorithm would, by definition,
refrain from introducing subjectivity into the process,1 but on
the other hand, it is the context that provides meaning to a
Bokhove and Downey 3
transcript. We follow this path in the understanding that a
researcher always will need to follow up any data processing
phase, like transcription (whether automated or not), through
application of professional judgement. Kvale (1996: 163)
raises the issue of transcriber reliability, indicating that in social
science research transcribers can employ different styles and
rendering of the transcription wording. Kvale (1996: 174) adds
that any attempt to include non-verbal cues in the transcript,
such as indicators of tone, mood and pauses, or other responses
such as laughter or giggling would only serve to exacerbate
issues of ‘intersubjective reliability’. It is likely that the produc-
tion of such detailed and high-quality transcripts would require
multiple phases of transcription to layer in detail and provide
opportunities for checking coverage and content. In a large-
scale study utilising qualitative data collection methods, involv-
ing multiple members in the research team, transcription can
produce thousands of pages of transcribed material (Hunt et al.,
2011), but even though the authors specifically report the chal-
lenges of managing large-scale qualitative datasets, virtually no
space is given to the discussion of the process of transcription
other than the need to capture separately the interviewer’s per-
ception of a respondent which can be lost in steps as early as
the transcription process. Hunt et al. (2011: 9–10) point out that
all members of the team, including ‘all senior researchers’ will
read significant numbers of full transcripts and go back to listen
to the raw audio recordings in the process of analysis.
Given these challenges, we assert that transcription in the
research process will always be a trade-off between available
time or means, and the quality of the transcript. A ‘better’ tran-
script, with ‘better’ being defined as the most complete and
trustworthy account of a media file, will be more costly. Given
this trade-off, perhaps the automated transcription of audio
can assist as a first or early step in the process, providing a suf-
ficiently ‘good enough’ first version. The use of such auto-
mated services could serve as a useful ‘first draft’ transcription
of audio data that would then form the foundation for what
Paulus et al. (2013) refer to as ‘cycles’ or ‘rounds’ of transcrip-
tion, which usually require the transcriber to engage with the
audio recording on multiple occasions in order to capture all
the required elements from the raw data (pp. 96–97). We
believe construction of a ‘first draft’ would also be relevant for
a range of transcription types, whether the aim be production
of a verbatim transcript (capturing features of speech and non-
speech), transcription types utilising a notation system (e.g.
Jefferson, 2004) to represent specific features of recorded talk,
or even for condensed or other gisted transcripts, allowing the
researcher to make choices as to what features to omit from
the transcript such as repetitions, false starts and other features
of natural speech (for types of transcription see Paulus et al.,
2013: 96–101). Auto-captioning applications are one means
by which researchers can gain access to sophisticated tools for
the automated recognition of speech. We have focused on this
route to obtaining a ‘first draft’ transcript because auto-cap-
tioning is widely and freely available as part of the panoply of
resources that support making the huge volume of audio-vis-
ual content available on the web more accessible to diverse
audiences. The process of auto-captioning can also carry with
it some additional technical advantages that we believe a
researcher may be able to exploit in order to enhance the utility
of the resulting transcripts. We will discuss these additional
benefits in more detail towards the end of the article.
Literature on automated captioning
The production of automated captions for videos with audio
tracks, entail the automatic recognition of speech in the audio
data, providing automatic subtitles for the audio belonging to a
video. The literature on auto-captioning can be divided under
two broad themes, namely literature related to various technical
aspects of automated captioning, and literature focusing on the
use of captioning to support students with additional educa-
tional needs, and thus supporting teaching and learning.
Technological developments. In their brief review of the history
of ASR, Juang and Rabiner (2004) describe how as far back as
the 1930s Bell Laboratories proposed a model for speech anal-
ysis and synthesis. Major advances in the statistical modelling
of speech in the 1980s led to widespread application of ASR in
situations where a human–machine interface was needed
(Juang and Rabiner, 2004). With ever increasing technological
improvements (for an overview, for example, see Ramírez and
Górriz, 2011) software solutions became available. Initially,
this was in the form of stand-alone software like Dragon Natu-
rally Speaking or IBM’s ViaScribe, later as part of other main-
stream, sometimes web-based programmes. By 2009,2 the first
version of video provider YouTube’s captioning system was
introduced, exemplifying a trend towards online solutions,
like IBM’s hosted transcription service. The development of
speech recognition systems has been rapid since then. All
major commercial speech recognition systems are based on
deep learning (e.g. see Deng and Yu, 2014). In recent years,
both Microsoft and IBM, with 5.9% and 5.5%, respectively,
approached the word error rate for humans, seen to be around
5% (Fogel, 2017; Tarantola, 2016). These advances have also
influenced captioning functions in YouTube. Deep learning
algorithms, for example, in 2017 for captioning sound effects
(Chaudhuri, 2017), have been used to further improve You-
Tube’s speech recognition quality. Whenever a new video is
now uploaded to YouTube, the new system runs and tries to
identify captions, including sounds. Given these rapid techno-
logical developments it was expected that perhaps the initially
sub-optimal experiences with speech recognition as a tool for
transcription of audio might have improved.
Methods of obtaining captions. There are several popular ways
to obtain captions. A first option is to ask professional compa-
nies to do this. This takes substantial time and is often accom-
panied by considerable costs (Dubinsky, 2014; Johnson, 2014).
A second option, used for more than a decade through tools like
Media Access Generator (MAGpie), Subtitle workshop and
Amara, is to manually make a subtitle file that can be used in
combination with the video. Recently, YouTube has managed
4 Methodological Innovations
to integrate these features within their own service. Finally,
there is the option of using auto-captioning services, with You-
Tube year-on-year improving the quality of this feature.
Another example is Synote (Wald, 2010) interfacing with text-
to-speech functionalities. In this scenario, text-to-speech soft-
ware generates the captions without human intervention
(Fichten et al., 2014). This option is the quickest option avail-
able, but there is debate on the accuracy of this approach (Ben-
nett et al., 2015; Parton, 2016). As technology progresses, it can
also be expected that accuracy will increase. Furthermore, the
degree of accuracy required from transcription of audio data
depends upon the nature of the transcript and their purpose.
Accuracy of captions. Nevertheless, even for a first draft, accu-
racy might be a problem (Johnson, 2014), but the reaction to the
severity of the inaccuracy is mixed, ranging from ‘devastating’
(Anastasopoulos and Baer, 2013), ‘a barrier to communication’
(Parton, 2016), ‘humorous’ (Clossen, 2014) to ‘a fairly good
job’ (Suffridge and Somjit, 2012). An often heard recommenda-
tion is to start with auto-captions and then edit to reduce the
number of errors and fix any timing issues (Clossen, 2014;
Johnson, 2014). In most cases, it is acknowledged that although
manual transcription might be superior, it is not realistic to think
the same amount could be done, because of time and money
constraints. Automated captioning can fail to accurately convey
the intended message (Barton et al., 2015; Johnson, 2014).
Recently, however, it has also been noted that performance of
the relevant algorithms seems to improve (e.g. Liao et al., 2013).
One way to further speed up and improve the accuracy of the
transcription process for interviews is to listen to the interview
and repeat what was said using voice recognition software
(VRS) that has been trained to recognise a specific voice. This is
the method used for live subtitling/captioning for television and
court reporting as well as for supporting deaf people in meet-
ings. The process is known as ‘respeaking’, ‘shadowing’ or
‘parroting’, but still involves at least the same time the recording
lasts. In this article, we hypothesise that the accuracy of auto-
mated captions might support the transcription process.
Supporting teaching and learning. The potential of automated
captioning to support teaching and learning for students with
special educational needs, including second language users, is
well recognised (e.g. Collins, 2013). Captioning can be seen as
supplementing video-based materials, for example, in the con-
text of a foreign language instructional tool (Dahbi, 2004). This
can also be a selection of words for captioning, so-called ‘key
word captioning’. Students’ understanding of the video content
can be increased, even if the complexity of the captioned key
words surpasses the reading level of the student (Ruan, 2015).
A study of deaf students (Shiver and Wolfe, 2015) suggested
that a large contingent of students preferred to watch videos
with automated captioning than with no captions. Parton (2016)
studied the use of captions in relation to deaf students. She
notes the role captions play in improving accessibility of video
resources, highlighting the legal obligations that (higher) edu-
cation institutions have to make materials accessible. Lewis
and Jackson (2001) have demonstrated that script comprehen-
sion of deaf and also hearing impaired students was greater
with captioned videos. Bain et al. (2005) describe key advances
in audio access that have occurred since 2000, mentioning the
intention to create real-time access for students who are deaf
and hard of hearing, without intermediary assistance. They uti-
lise a tool called Viascribe to convert speech recognition output
to a viable captioning interface. Federico and Furini (2012)
also focussed on students with some form of additional need
(e.g. hearing impaired, dyslexic and English as a Second Lan-
guage (ESL)), proposing the use of off-the-shelf ASR software.
Wald (2005) seems to broaden the target audience, acknowl-
edging that ASR can ‘assist those who require captioning or
find notetaking difficult, help manage and search online digital
multimedia resources and assist blind, visually impaired or
dyslexic people by augmenting synthetic speech with natural
recorded real speech’ (p. 1) or even more general ‘anyone who
needs to review what has been said (e.g. at lectures, presenta-
tions, meetings etc.)’ (Wald and Bain, 2007: 446). Ranchal
et al. (2013) also extend the potential benefits of ASR for stu-
dents who have difficulty taking notes accurately and indepen-
dently, particularly for non-native English speakers and
students with disabilities.
A proof-of-concept: voice and speech recognition tools in the lit-
erature. There is limited literature on the use of VRS and
ASR tools to aid transcription. Any attempts to utilise VRS
tools on raw audio from multivoice interviews usually result
in expression of exasperation due to woefully low accuracy
rates (Dempster and Woods, 2011; Dresing et al., 2008;
Evers, 2011). Some authors have utilised an approach in
which the researcher simultaneously listens to the original
voice recording while dictating/reciting into VRS trained to
recognise the researcher’s own voice, in a manner analogous
to the ‘respeaking’ approach to captioning described above.
Such articles usually report very small-scale, personal com-
parisons of the time commitment required to conduct the
VRS dictation method versus standard listen-and-type tran-
scription approaches (Matheson, 2007). Johnson (2011) has
argued that traditional listen and type requires less time than
simultaneous dictation via VRS. To further support the tran-
scription process, sometimes custom applications are used;
for instance, Roberts et al. (2013) managed data in Synote, a
freely available application enabling synchronisation of
audio recordings with transcripts and coded notes.
Some researchers (e.g. Evers, 2011) have questioned the
need to transcribe audio recordings at all, now that it is possible
to add analytical codes directly onto raw digital files for all sorts
of media, including audio files, using tools such as ATLAS.ti,
MAXqda, NVivo and Transana. Nevertheless, even proponents
of bypassing transcription such as Evers (2011) report issues
with alignment of codes to specific data segments in the audio
(or video) files as this is much harder than coding segments on
a typed transcript. She also relates issues with reduced opportu-
nity for reflection that arises when coding directly onto the raw
audio file compared with the multiple stages of transcription
Bokhove and Downey 5
and analysis. Conscious of a ‘generation effect’ in terms of
exposure to technology, Evers (2011) asked her students, as well
as colleagues to compare the experience of traditional transcrip-
tion versus direct coding onto audio files. While the students
were vocal about the benefits of time saved by not requiring a
transcript, and also the closeness that they established with the
participant’s voice when coding directly, they complained about
problems of losing contact with sections of data that they never
listened to a second time and a perception of sloppy rephrasing
of a participant’s words. Some of the students even resorted to
producing traditional transcripts for at least some sections of the
data. Also noteworthy were indications that the visual nature of
analysis of transcripts aids the researcher in tracking the analyti-
cal process, which is not (currently) replicated by the practice of
direct coding onto audio file segments. The searchable nature of
transcripts was frequently given as a key benefit over direct cod-
ing onto the audio file.
Ranchal et al. (2013) have studied the use of ASR tools in
the context of lecture capture. In their study, they measured
the accuracy of the ASR technologies using word error rate
and recognition accuracy tools. Even after voice profile
training on ViaScribe ASR software, they were only able to
achieve accuracy rates close to 80%. Using a speaker inde-
pendent post lecture transcription tool (IBM Hosted
Transcription Service) that utilised a double pass approach,
accuracy was increased to between 85% and 91% (Ranchal
et al., 2013: 307). They then employed grad student teach-
ing assistants to correct word errors in the resulting auto-
mated transcripts. An automated transcript with a word error
rate of over 20% still required a teaching assistant unfamil-
iar with the course materials to spend up to 4 hours per hour
of lecture audio to correct word errors (pp. 306–307).
Kawahara (2012) reports on a bespoke speaker-independ-
ent ASR system developed for the production of transcripts of
plenary and committee meetings of the Japanese Parliament.
The committee meetings are particularly challenging as they
consist of multiple voices engaged in free speaking with
interaction between speakers. Despite these challenges, the
system is reported as consistently producing accuracy levels
of at least 85% and, more commonly, approaching 90% for
committee meetings and over 95% for plenary sessions.
Parliamentary reporters further process the transcripts to cor-
rect errors in the automated transcripts using a post-editor
tool that the reporters helped the development team to design.
The resulting editing process produces a searchable archive
file that consists of the transcribed text, together with the raw
speech audio and video files that are aligned and hyperlinked
by the speaker name and the words uttered (Kawahara, 2012:
2227). Kawahara indicates that the accuracy of the ASR tool
is regularly monitored, and the lexical and language models
used within the ASR are revised annually by the same parlia-
mentary reporters who edit the automated transcripts.
The aim of the proof-of-concept described in this article is
to consider whether we might utilise the functionality of
freely available automated captioning services to save time
in the research process, especially in the transcription pro-
cess for audio recordings. To our knowledge, there has not
yet been such a direct application of automated captioning to
support the laborious and time-consuming transcription pro-
cess in a research context.
Methodology
The methodology section tries to faithfully present the complete
procedure of automatically transcribing three types of audio
recording: two one-to-one interviews, a group interview and a
recording captured as part of the observation of a lesson taught in
a school. Note that, in this context, we use the terms ‘audio’ and
‘video’ interchangeably; it denotes how even when we only ana-
lyse audio, the described procedure requires uploading a video.
However, this can be achieved by the addition of a single static
image for the duration of the audio recording. This is a practice
commonly used to enable audio files to be uploaded to YouTube.
Collecting the data
The three data sources used for this proof-of-concept were
publicly available resources, for which we gained secondary
data ethics approval from the University’s ethics board (num-
ber 26617). Ethical aspects of the methods described in this
study always need to be taken into account, as we discuss
further towards the end of the article. We will refer to the
three sources as T, C and I.
Source T consists of two one-to-one interview videos
used in one of our methodology courses available on our
department’s YouTube channel.3 The videos (with audio)
contain interviews with two former teacher practitioners.
The videos were downloaded in mp4 format and were cap-
tured using studio-quality radio lapel microphones with
audio captured in Dolby Digital (AC3) format with data rates
of 256 kbits per second.4 Existing verbatim transcripts of
each interview were also available, that had been produced
manually by a teaching assistant. Figure 1 shows a fragment
of the format of the existing transcript.
Figure 1. fragment of the existing transcription (anonymised).
6 Methodological Innovations
Figure 2. fragment of the PDF transcript of the interview with
General the Lord Walker of Aldringham.
Figure 3. screenshot of the YouTube interface for captions.
Source C consists of a classroom video from the TIMSS
1999 video study, downloaded from the TIMSS website.5
The TIMSS study focused on grade eight mathematics and
science teaching in seven countries, in which national sam-
ples of teachers were videotaped teaching an eighth-grade
lesson in their regular classrooms. The website allows the
download of both mp4 format videos, as well as transcripts
in text format. The mp4 video of the US1 lesson was down-
loaded, consisting of audio and video. US1 is an USA eighth
grade mathematics lesson which focuses on graphing linear
equations, is 44 minutes in duration, and with 36 students
enrolled in the class. The audio contains teacher talk, group
dialogue and a fair amount of background sound. The tran-
scripts were produced manually, based on protocols in a
transcription manual.6
Source I consists of a video from the Chilcot Iraq Inquiry
in the form of an interview with General the Lord Walker of
Aldringham.7 The website also contains a transcript of the
interview in PDF format, see Figure 2, which was stored as a
text file. The exact method of transcript creation for this
source is unknown; the protocols of the Inquiry seem incon-
clusive.8 However, as the final transcript on the website has
been validated, we assume it is deemed a trustworthy account
of the hearing.
Applying automated captioning
The auto-captioning and caption-processing tools described
below are all freely available, web-based tools. For the purpose
of implementing the proof-of-concept videos T, C and I were
uploaded to the private YouTube channel of the first author
with the option of ‘automatic captions’ in English selected.
Figure 3 shows an impression of the YouTube interface for
uploading Source I.
After uploading the videos were left for a couple of
hours to let the captioning engine create automated cap-
tions. Through websites that allow the downloading of
these captions, like http://mo.dbxdb.com/Yang, YouTube
themselves and www.diycaptions.com, the transcripts
were downloaded. In most cases, captions could be down-
loaded in two formats: a ‘text only’ format, and a time-
stamped subtitle file, often with the file extension .srt as
shown in Figure 4.
To make the text files comparable to the existing man-
ually produced transcripts, decisions had to be made as to
what elements of the captioning to include. The caption
file (a so-called .srt file) includes timestamps, for exam-
ple, and the original transcripts include names or initials
of the speakers. With a programme called ‘Subtitle Edit
3.5.2’9 the timestamps and durations were removed prior
Bokhove and Downey 7
to measuring the match between documents. In a real
transcription setting, it might be a useful addition to the
transcript to retain the timestamps to aid reconnection
with the raw audio file. The inclusion of timestamps
might facilitate connection between elements of the tran-
script and the corresponding section of raw audio file in
the way that computer assisted qualitative data analysis
software (CAQDAS) tools have increasingly made pos-
sible (Paulus et al., 2013: 99). This can be particularly
Figure 4. fragment of a time-stamped sub-title file, obtained
from YouTube.
Figure 5. exporting the automated transcript in plain text format.
useful when constructing specific forms of transcript that
require multiple cycles of engagement with the raw audio
file to capture the level of detail required in the transcript,
or to determine what information might be omitted in a
gisted form of transcript.
For the core text from the automated transcripts, plain text-
only versions were created, as demonstrated in Figure 5. We
were aware that there most certainly would be some differ-
ences between automated and manually produced transcripts
in formatting, but accepted this as ‘less-than-perfect’ out-
comes that could simply arise from formatting issues. For
each of the data sources, T, C and I we will present quantita-
tive measures of the similarities between automated and man-
ually produced transcripts, together with some qualitative
description of the differences.
Results: comparing text similarity
There is a large variety of tools available to compare text
similarity. For this pilot, we initially used the open source,
windows-based WCopyfind 4.1.5 which is an open source
windows-based programme that compares documents and
reports similarities in their words and phrases.10 One chal-
lenge with this software is that the settings should be tweaked
to get a good match. To illustrate this, using default values,
8 Methodological Innovations
Figure 6 shows that there is more overlap between the auto-
mated transcript at the top and the original transcript at the
bottom, than the red text indicates.
For example, at the top ‘That’s a good question’ appears
in both transcripts but is not flagged up as equal. The same
applies to ‘Do you think you have always had those traits?’
The differences can be explained by the numerous options
the software has to compare texts, and the various ways in
which slight differences can come up as ‘different’. Examples
of what WCopyFind can take into account in determining
what constitutes a difference are as follows: punctuation,
numbers, cases, non-words, word length, and more. Despite
this, comparison of the automated and manual transcripts
showed 69% and 64% similarity, respectively. Nevertheless,
for a better comparison, we turned to different software, as
described in the next section.
The two interviews (source T)
To obtain a more sophisticated comparison we utilised the
well-known plagiarism detection software from Turnitin
(2017). First, the text from the automated transcript was
uploaded, and after that the manual transcription. We made
sure that no other sources were counted in the percentage
match.
Table 1 shows that both automated and manual tran-
scripts show a very high level of agreement. Further scru-
tiny of the comparison in Turnitin showed that many
discrepancies were caused by relatively minor typos in the
original, such as incorrect automated transcription of
domain-specific words and names, as demonstrated in
Table 2. This list is not meant as an exhaustive analysis of
the transcripts but as demonstration that many of the errors
actually are quite small and easily rectifiable. Small differ-
ences also occurred the other way, that is, that Turnitin
would not flag them up while they were different from the
recording.
It can be observed that the differences are minor, and
mainly concern easily rectified issues or aspects that manual
transcripts from human transcribers might not necessarily
result in the optimal transcript.
Figure 6. Comparison of output of automated transcript (top) and the original transcript (bottom).
Table 1. Similarity between automated and manual transcripts
for two interviews (pseudonyms used).
Adams Barnett
Word count automated 934 1816
Word count manual 947 1817
Turnitin % similarity 91% 92%
Bokhove and Downey 9
The TIMSS video study (source C)
The results of the similarity check of transcripts derived from
the TIMSS classroom study video are presented in Table 3.
These are also quite favourable but with a similarity match of
68% the result is not as good as that achieved for the inter-
view transcripts from Source T.
Table 2. Selection of qualitative differences between the manual
and automated transcripts for (T). The ‘correct’ interpretation is
indicated in italics.
Manual Automated Comment
Staid
Carrier
Consciences
Stayed
Career
conscientious
The automated transcript did
not contain language typos or
incorrectly interpreted words.
I am I’m This means the same but is
picked up as ‘different’.
Head teacher Headteacher The similarity check saw the
two as different.
Completer Complete a The automated process
confused some of the sounds.
The interviewee used the
word ‘completer’ in the
context of ‘being someone
who completes things’.
Text added by
software for
downloading
the sub-titles
The similarity check saw these
as textual differences.
This text had
initials of the
speakers
The similarity check saw these
as textual differences.
Table 3. Similarity between automated and manual transcripts
for the classroom video.
US1 lesson
Word count automated 5659
Word count manual 5830
Turnitin % similarity 68%
Table 4. Selection of qualitative differences between the manual and automated transcripts for (C). The ‘correct’ interpretation is
indicated in italics.
Manual Automated Comment
Three page free paste The automated process confused some of the sounds.
You’re You The automated process confused some of the sounds.
Every one of you everyone you The automated process confused some of the sounds.
There Here The automated process confused some of the sounds.
The –use the ruler, Robert. Use the
ruler man. Make it neat. All right?
his easily make it mean when
you put like a one in here
what you’re like
This is completely different. The predictive algorithm did
not understand this at all.
Numbers e.g. 2 thirds or 2/3 The automated algorithm does not process numbers well.
try with this one Tribalism The automated process confused some of the sounds.
You’ve already forgotten You void forgotten The automated process confused some of the sounds.
Table 4 gives a selection of the types of differences in the
two transcripts. It can be observed that the differences are
more substantial than with the interview transcripts (Source
T). Some sounds clearly have not been picked up correctly by
the captioning algorithm. Another challenge lies in specific
domain knowledge, for example, the mathematical content
(numbers, especially fractions) that are not picked up.
Surprisingly, the predictive algorithm did manage to correctly
transcribe domain-specific terms such as ‘y-intercept’.
The quality of the audio for Source C is inferior to that of
source T, but given the fact that the audio recording is from a
single microphone located in a busy classroom environment
from a recording made using equipment and technology avail-
able in 1999, the resulting transcript seems decent. In these
examples, the colloquial language sometimes adopted in class-
room dialogue seems to be a challenge for ASR software.
Chilcot recording (source I)
Finally, the Chilcot recording, by far the longest of the
recordings, also showed an almost two-third similarity match
between the manual and automated transcripts, as indicated
in Table 5.
However, the original manual transcript was by far the
most contextualised data in that it followed a standard report-
ing convention for the inquiry proceedings. For example, the
lead names indicating who said what were systematically
included in the text of the manual transcript, as were line
numbers. A qualitative comparison of the first pages indi-
cated similar challenges for the ASR as those observed in the
Table 5. Similarity between automated and manual transcripts
for the Chilcot interview video.
Chilcot
Word count automated 14,187
Word count manual 16,587
Turnitin % similarity 66%
10 Methodological Innovations
comparison of the classroom study transcripts (Source C), as
Table 6 demonstrates.
Although there were more differences between transcripts
in this case than with those derived from the interview and
classroom settings, here again many of the difference
observed do not seem particularly problematic in terms of
their impact on meaning, especially to an informed reader
such as a researcher moving to the process of reviewing the
transcript, or even to the process of coding the data. All three
data sources seem to confirm that with minor effort one
might be able to obtain reasonable ‘first version’ automated
transcripts through the use of freely available web services.
Conclusion and discussion
This article set out to explore, as a proof-of-concept,
whether it would be possible to use freely available fea-
tures of web-based tools for automated captioning to pro-
duce automated transcripts of audio and video recordings.
Our conclusion is that this indeed is possible and that this
results in a reasonable first version of a transcript. If we
take the conservative estimate that about two-thirds of the
transcript, without any editing, can be obtained with a cou-
ple of minutes of uploading, a few hours of waiting time
(that can, of course, be devoted to other tasks), and a min-
ute of downloading, it is clear that the time savings should
be substantial. For high-quality audio in optimal settings
such as those used for one-to-one interviews the percent-
age match to manually produced transcripts can be even
higher, surpassing 90%. Even with the possibility of such
high accuracy rates, indicated by percentage of matching
text discussed here, we are certainly not suggesting that
auto-captioning would be the end of the transcription pro-
cess; rather, it would facilitate it.
Table 6. Selection of qualitative differences between the manual and automated transcript for (I). The ‘correct’ interpretation is
indicated in italics.
Manual Automated Comment
Their
In adjusting
To imprint inary
they’re
into justing
Two preliminary
The automated process confused some of the
sounds.
Recognise recognize American-English spelling differences
What we hear won’t be here This example was tabulated separately because
errors can of course change the meaning
completely.
Freedman Friedman Names are problematic, although it did pick up a
name.
But of course, that was in 1991, so one might
have hoped that, by 2003, those sorts of
problems had been overcome.
because that was in nineteen one no
one might have hoped that by 2003
the problems you know to come well
As with spoken numbers, years present problems
here, specifically the tendency in speech to split a
four digit year into a pair of two digit numbers.
SIR LAWRENCE FREEDMAN: Did you feel
that it was creating risks for your forces?
GENERAL THE LORD WALKER: It was
obviously creating some risks, but we are used
to dealing with risk.
did you think this was creating risks for
your forces well it was obviously creating
some risks but I think I mean we ‘re used
to dealing with risk
The meaning of these two statements is similar
but it demonstrates how the names in front of the
statements result in a difference being flagged up.
Saving time in the research process
In large-scale research projects generating a substantial vol-
ume of audio recording, outsourcing the process for the pro-
duction of a first version transcript, is more commonplace.
Even with outsourcing the process of transcription to highly
experienced transcribers, it is by no means clear that the qual-
ity of the transcripts will be perfect. After all, audio is not
always clear and transcribers recruited from outside the
research team are likely to be unfamiliar with the contextual
information of any audio recording, making transcription of
some domain-specific sections of audio data particularly
challenging. It is not unusual for members of the research
team to review and edit externally produced transcripts as a
quality check and also to aid in establishing closeness to the
data. Indeed, the production of some forms of transcript (e.g.
Jeffersonian) are considered to require several cycles of
engagement with the raw audio data in order to capture the
range of information required in the transcription (Paulus
et al., 2013). One might argue that the editing of a first version
automated transcript, produced using auto-captioning tools,
would be analogous to the review that would normally be car-
ried out on externally produced transcripts, and that even for
smaller scale research projects the benefits of time saved in
transcribing might be usefully invested in such a review pro-
cess. Compared to outsourcing transcription, the automated
process would also be very cheap, and is currently free, based
on available tools. Some tools, like aforementioned www.
diycaptions.com even provide an interface, not unlike exist-
ing CAQDAS tools, to manually correct any outstanding
errors. Notwithstanding the evolution of the technology used,
we think that considering the appropriateness of an automated
process first, before going through the traditional, lengthy
process, can be one of the enduring principles of our article.
Bokhove and Downey 11
Issues and advantages arising from automated
transcription
This study highlights several challenges. First, as flagged up
before, there are circumstances under which the automated
transcription works less well. If the expectation is that one
obtains perfect transcripts, then there will be disappointment.
As the examples have shown, errors are more likely to occur
with given names, differences in formatting and domain-
specific terms. These errors all seem to relate to the complex-
ity and quality of the audio recording. Factors that seem to
influence this are as follows: the number of different speak-
ers, the way turn-taking is organised (i.e. interruptions vs
sequential speech), accents of speakers, how colloquial the
language is, and even the audio quality of the recording.
From a technical point of view, these challenges have been
known for a while. For instance, Kawahara et al. (2003)
acknowledged that the performance of ASR is affected by
factors such as acoustic variation caused by fast speaking
and imperfect articulation, linguistic variation such as collo-
quial expressions and disfluencies. Byrne et al. (2004)
suggested that ‘adequate accuracy … of spontaneous conver-
sational speech’ could be obtained (p. 433). In the last dec-
ade, as we have reported in the literature section, accuracy of
the ASR has improved considerably, and we suggest that
automated transcription is starting to become a feasible solu-
tion for research purposes: ‘good enough’ transcripts that can
then be perfected with far less manual labour than before. A
second challenge, in this article, might concern our method
of determining the similarity between documents, which
relies on Turnitin. The Turnitin algorithms are proprietary;
therefore, we can’t say on what basis the similarity is calcu-
lated. Nevertheless, as Turnitin is used widely to check simi-
larity for the purpose of academic integrity checks, we feel it
is reasonable to assume that the algorithms give a fair indica-
tion of the similarity. As indicated before, simpler methods to
measure similarity between text extracts like those using the
default values in WCopyFind, still yield percentage matches
up to 70% for the best case interviews (Source T). In our
view, it is reasonable to see this as a minimum, as we also
established that this comparison method did not flag up some
of those similarities. In the context of producing ‘good
enough’ first transcripts, we think our approach sufficiently
supports our conclusions regarding similarity. In our view,
automated transcription, whatever technology used, should
be a viable option for any researcher, with a manual data
check still always in place.
Future developments for automated transcripts
As well as the auto-captioning tools described here, other
freely available tools might offer possibilities for the genera-
tion of automated transcripts. Voice recognition software asso-
ciated with the operating systems running on common mobile
computing devices such as tablets and smartphones are
increasingly able to render speech to text without any of the
training to a specific voice that was required by older VRS
systems. These systems would need to be coupled to playback
of an audio recording, to generate automated transcripts as
such technologies work by ‘listening’ to a file in real time as
opposed to processing the complete digital file in the way that
the auto-captioning tools process the audio data. The VRS
tools built into common operating systems for mobile devices
currently have to buffer after a set period of ‘listening’ to
speech in order to process the acquired data, which would
make the process awkward and time consuming, but it may be
that the application programming interfaces (APIs) that pro-
vide the VRS functionality can provide real time processing in
parallel with the process of data capture via ‘listening’ to the
playback of a recorded audio file. As well as processing, the
complete digital file rather than capturing data by ‘listening’ in
real time, another advantage of the captioning software
described here is that it can automatically add timestamps to a
transcript, which might then be used to facilitate matching
between the transcript and the raw data at specific points in the
audio file, demonstrated by the utilisation of subtitling files.
Automated transcriptions might cover different languages;
already YouTube’s automated captions are available in English,
Dutch, French, German, Italian, Japanese, Korean, Portuguese,
Russian and Spanish. Not all of the languages are as accurately
captured as the examples in this article, but with recognition
techniques improving, different languages become available
for that first ‘good enough’ transcription. Although the recogni-
tion of the lower quality TIMSS video (C) already was fairly
high, it might be expected that the recognition quality might
only increase and the higher quality audio derived from equip-
ment available in recent years, exemplified by the audio record-
ings of the one-to-one interview sources used in this
proof-of-concept, indicate that raw data using contemporary
audio quality standards combined with automated transcription
might yield very high-quality first transcripts in best case sce-
narios. Finally, another recent development is the integration of
social networking tools into the captioning services for crowd-
sourcing captions that might yield higher quality outputs.
Given the technological strides of the last decade, it is envis-
aged that the possibilities will only improve; another reason for
us to suggest that it at the very least automated transcription
should be considered as an option in the research process.
Ethical considerations
Finally, it is also important to consider ethical aspects, for
example, regarding data protection and security issues.
Captioning services, whether embedded in a tool like
YouTube, or providing support for other tools, might store
data on computers other than institutional ones. According to
the Economic and Social Research Council (ESRC) these
aspects form part of data protection and should be reviewed
regularly (ESRC, 2017). Specifically for subtitles, a poignant
example of a security issue, are the recent hacking incidents
12 Methodological Innovations
that used so-called ‘subtitle servers’ to break into computers
(Biggs, 2017). There also are ethical considerations regarding
the location and safe storage of data on third parties servers:
do research council rules allow storage of personal data on
these? This has become particularly relevant following recent
developments with Facebook and Cambridge Analytica (e.g.
Greenfield, 2018) and the General Data Protection Regulation
(GDPR), a regulation in EU law on data protection and pri-
vacy for all individuals within the European Union (EU) and
the European Economic Area (EEA). One consequence of
this is that data must not be available publicly without explicit,
informed consent, and cannot be used to identify a subject
without additional information stored separately. Of course,
in the context of YouTube, even as unlisted or private videos,
this means ensuring that these requirements are met. In gen-
eral, it is important that researchers are fully cognisant of the
ethical and security implications of the research and analyti-
cal methods they use. Whether using primary or secondary
data with these tools, university ethics committee approval
should firmly be in place, to ensure sufficient consideration of
the ethical aspects. As stated before, we also did this for this
study. A more sociological and ethical aspect might also be
seen in the relation with the ‘future of work’ and the labour
involved in transcription. Srnicek and Williams (2015), for
example, argue that the crisis in capitalism’s ability (and will-
ingness) to employ all members of society, should lead to
investment in labour-saving technologies. They envisage a
positive feedback loop between a tighter supply of labour and
technological advancement. In that light the wider implica-
tions of our ‘work saving’ propositions could be considered.
Notwithstanding these challenges, we suggest this study
has shown the promise of automated generation of ‘good
enough’ transcripts. In our view, it would be a ‘common
sense’ approach to analyse qualitative data at scale.
Declaration of Conflicting Interest
The author(s) declared no potential conflicts of interest with respect
to the research, authorship, and/or publication of this article.
Funding
The author(s) received no financial support for the research, author-
ship, and/or publication of this article.
Notes
1. Note that some point out that the algorithm might not be biased
but the programmer of that algorithm, for example, see O’Neill
(2016).
2. https://googleblog.blogspot.co.uk/2009/11/automatic-cap-
tions-in-youtube.html
3. YouTube: www.youtube.com
4. Radio mics were Sennheiser EW 100-ENG G3 and the camera
used to capture the video and audio was a Panasonic AVCHD.
5. www.timssvideo.com
6. https://timssvideo.squarespace.com/s/Transcription-Manual.pdf
7. http://www.iraqinquiry.org.uk/the-evidence/witnesses/w/
general-the-lord-walker-of-aldringham/
8. http://www.iraqinquiry.org.uk/the-inquiry/protocols/
witnesses-giving-evidence/
9. SubTitle Edit can be found at https://github.com/SubtitleEdit/
10. http://plagiarism.bloomfieldmedia.com/wordpress/software/
wcopyfind/
ORCID iD
Christian Bokhove https://orcid.org/0000-0002-4860-8723
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Author biographies
Christian Bokhove is associate professor in Mathematics Education
with research interests in everything regarding mathematics teach-
ing in secondary schools. In addition, he likes to apply novel
research methods to educational and classroom contexts.
Christopher Downey is associate professor of Education with
research interests in educational effectiveness and improvement; par-
ticularly the use of data to inform expectations and decision-making,
the implications of educational accountability and our understanding
of social processes associated with learning and educational practice.
