Producing 'good enough' automated transcripts securely

Extending Bokhove and Downey (2018) to address security concerns

Datos Bibliográficos

ID4833339
AutoresJoseph Da Silva (0000-0002-4153-6237, Royal Holloway University of London, autor de correspondencia)
Año2021
Volumen14
Número1
Fecha de publicación2021-01-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaMethodological Innovations (JOURNAL)
Identificadores de la revistaISSN: 2059-7991 • E-ISSN: 2059-7991
EditorialSAGE Publications Inc (PUBLISHER)
DOI10.1177/2059799120987766
OpenAlexW3128070751
ISBN9799120987766
IdiomaEN
Citas recibidas7
Referencias citadas48

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

Cloud computing · Computer security · Confidentiality · Data science · Internet privacy · Qualitative research · Quality (philosophy · Sociology · Task (project management · Transcription (linguistics · Computer Science · Engineering · Focus Groups and Qualitative Methods · Hate Speech and Cyberbullying Detection · Social Media and Politics