From voice to ink (Vink): development and assessment of an automated, free-of-charge transcription tool

BACKGROUND

Verbatim transcription of qualitative audio data is a cornerstone of analytic quality and rigor, yet the time and energy required for such transcription can drain resources, delay analysis, and hinder the timely dissemination of qualitative insights. In recent years, software programs have presented a promising mechanism to accelerate transcription, but the broad application of such programs has been constrained due to expensive licensing or "per-minute" fees, data protection concerns, and limited availability of such programs in many languages. In this article, we outline our process of adapting a free, open-source, speech-to-text algorithm (Whisper by OpenAI) into a usable and accessible tool for qualitative transcription. Our program, which we have dubbed "Vink" for voice to ink, is available under a permissive open-source license (and thus free of cost).

RESULTS

We conducted a proof-of-principle assessment of Vink's performance in transcribing authentic interview audio data in 14 languages. A majority of pilot-testers evaluated the software performance positively and indicated that they were likely to use the tool in their future research. Our usability assessment indicates that Vink is easy-to-use, and we performed further refinements based on pilot-tester feedback to increase user-friendliness.

CONCLUSION

With Vink, we hope to contribute to facilitating rigorous qualitative research processes globally by reducing time and costs associated with transcription and by expanding free-of-cost transcription software availability to more languages. With Vink running on standalone computers, data privacy issues arising within many other solutions do not apply.

Transcription and Qualitative Methods: Implications for Third Sector Research

While there is a vast literature that considers the collection and analysis of qualitative data, there has been limited attention to audio transcription as part of this process. In this paper, I address this gap by discussing the main considerations, challenges and implications of audio transcription for qualitative research on the third sector. I present a framework for conducting audio transcription for researchers and transcribers, as well as recommendations for writing up transcription in qualitative research articles.

Role-Play as Responsible Robotics: The Virtual Witness Testimony Role-Play Interview for Investigating Hazardous Human-Robot Interactions

The development of responsible robotics requires paying attention to responsibility within the research process in addition to responsibility as the outcome of research. This paper describes the preparation and application of a novel method to explore hazardous human-robot interactions. The Virtual Witness Testimony role-play interview is an approach that enables participants to engage with scenarios in which a human being comes to physical harm whilst a robot is present and may have had a malfunction. Participants decide what actions they would take in the scenario and are encouraged to provide their observations and speculations on what happened. Data collection takes place online, a format that provides convenience as well as a safe space for participants to role play a hazardous encounter with minimal risk of suffering discomfort or distress. We provide a detailed account of how our initial set of Virtual Witness Testimony role-play interviews were conducted and describe the ways in which it proved to be an efficient approach that generated useful findings, and upheld our project commitments to Responsible Research and Innovation. We argue that the Virtual Witness Testimony role-play interview is a flexible and fruitful method that can be adapted to benefit research in human robot interaction and advance responsibility in robotics.

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

Interviews are an established research method across multiple disciplines. Such interviews are typically transcribed orthographically in order to facilitate analysis. Many novice qualitative researchers’ experiences of manual transcription are that it is tedious and time-consuming, although it is generally accepted within much of the literature that quality of analysis is improved through researchers performing this task themselves. This is despite the potential for the exhausting nature of bulk transcription to conversely have a negative impact upon quality. Other researchers have explored the use of automated methods to ease the task of transcription, more recently using cloud-computing services, but such services present challenges to ensuring confidentiality and privacy of data. In the field of cyber-security, these are particularly concerning; however, any researcher dealing with confidential participant speech should also be uneasy with third-party access to such data. As a result, researchers, particularly early-career researchers and students, may find themselves with no option other than manual transcription. This article presents a secure and effective alternative, building on prior work published in this journal, to present a method that significantly reduced, by more than half, interview transcription time for the researcher yet maintained security of audio data. It presents a comparison between this method and a fully manual method, drawing on data from 10 interviews conducted as part of my doctoral research. The method presented requires an investment in specific equipment which currently only supports the English language.