Automated generation of “good enough” transcripts as a first step to transcription of audio-recorded data

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 paper 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.

Producing ‘good enough’ automated transcripts securely: Extending Bokhove and Downey (2018) to address security concerns

“…This article supports the argument that there is significant benefit to qualitative researchers from using automated transcription; this argument itself is not novel, for example, Bokhove and Downey (2018) and Moore (2015). However, I have presented a method that is not just effective but also addresses the security concerns arising from the use of cloudbased services and can be achieved at relatively low cost (assuming multiple interviews are planned).…”

“…There was no discernible difference in transcription accuracy between male and female voices, however, there was only one female interviewee in this sample and, therefore, this is far from conclusive. Gender bias in voice recognition systems has been previously established, as described by Howard andBorenstein (2018: 1525), and such bias has been identified by Tatman (2016) in the specific software used by Bokhove and Downey (2018). While Bokhove and Downey (2018) and I utilise different specific software applications, both are provided by Google and may be based on the same technology 22 and therefore both may suffer from the same bias Tatman (2016) identifies, which could further limit this research.…”

“…Prior to Bokhove and Downey (2018), Moore (2015) and Matheson (2007) utilised transcription software that did not rely on cloud computing. Moore identified that such software was inconsistent in terms of accuracy but, similar to Bokhove and Downey (2018), was effective in generating a 'first-pass transcript' (Moore, 2015: 253). Moore (2015: 269-270) predicted that capabilities in this field would continue to advance, and also suggested that 'partial automation', in particular, would be more desirable than full automation, which aligns with the method I explore below.…”

“…I felt better about the transcriptions as they progressed (as captured in my fieldnotes 10 ) but began to consider the use of automated transcription software for subsequent interviews, at least to get the core text down. As someone who works in a heavily technology-dependent profession and industry, I was aware of advances in the field of voice-to-text as summarised by Bokhove and Downey (2018: 3). However, as a security professional, I was also very aware that these newer software offerings relied predominantly on cloud-based capabilities, for example, as used by Bokhove and Downey (2018) and were predominantly zero-cost; these factors led me to consider such services cautiously and to review the terms and conditions and privacy policies of these in detail.…”

“…As someone who works in a heavily technology-dependent profession and industry, I was aware of advances in the field of voice-to-text as summarised by Bokhove and Downey (2018: 3). However, as a security professional, I was also very aware that these newer software offerings relied predominantly on cloud-based capabilities, for example, as used by Bokhove and Downey (2018) and were predominantly zero-cost; these factors led me to consider such services cautiously and to review the terms and conditions and privacy policies of these in detail. Cloud-based solutions are not necessarily any less secure than non-cloud alternatives; however, any system that is under the administrative control of others presents de facto data privacy challenges, given those administrators have access to the system (Kamarinou et al, 2016: 188-189).…”