Battaglia, Fabio (2024) From “listen and repeat” to “listen and revise”: how to transcribe interviews offline quickly and for free using voice recognition software. International Journal of Qualitative Methods, 23. ISSN 1609-4069
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Abstract
Transcribing interviews is one of the most time-consuming and alienating tasks in qualitative research. Some have tried to bypass the problem by hiring external transcribers, which however can be very expensive. Whether due to the time and energy or the financial investment that transcribing requires, researchers therefore often self-impose a limit on the number of interviews that they conduct or even refuse to conduct interviews altogether. To prevent this and help reduce transcription fatigue, some scholars have developed the so-called “listen and repeat” technique. This involves speaking into a microphone what one hears through their headset while a voice recognition software transcribes word by word what it hears. However, this technique has many limitations and still requires a considerable amount of time and effort to be put by the researcher. This article introduces an alternative transcription technique which helps overcome these problems thanks to recent advancements of Artificial Intelligence in the field of voice recognition. Although the drawbacks and unintended consequences of Artificial Intelligence are often highlighted, this article explores its use for interview transcription showing that it can improve drastically the work and life of qualitative researchers. More specifically, this article introduces a transcription technique which allows to generate transcripts fully offline (avoiding in so doing the security concerns that the rising number of cloud-based transcription platforms often raise), rapidly and at little to no cost which one only needs to revise whilst listening to interview recordings, which is why I call this the “listen and revise” technique.
Item Type: | Article |
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Additional Information: | © 2024 The Author |
Divisions: | Social Policy |
Date Deposited: | 09 Apr 2024 11:09 |
Last Modified: | 15 Nov 2024 05:36 |
URI: | http://eprints.lse.ac.uk/id/eprint/122577 |
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