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Automated transcription for qualitative research
24/06/2021 at 12:15 - 13:15
Caitlin Hafferty: Automated transcription for qualitative research
Transcription is often an arduous task for researchers; a process which is typically completed manually or outsourced to costly services. This process is essential for qualitative researchers, for example, who seek to generate interview transcripts, produce written and annotated meeting notes, or to transcribe videos and other recordings. While manual transcription has numerous benefits, not least with regards to the interpretation of meaning from data, it is very time-consuming and expensive. Transcription software, referring to an entirely automatic solution which helps human transcribers convert audio recordings to written text, is rapidly advancing and has huge potential to transform this essential research task.
However, the methodological and ethical considerations of automated transcription remain a complex, highly context-dependent, and relatively unknown area. There are numerous potential concerns relating to accuracy and reliability, barriers to interpretation and familiarisation with data, ethical issues and challenges relating to data protection (GDPR) and safe storage. Drawing from her experience using transcription apps during her PhD, Caitlin shares some insights and important considerations regarding their use in a research setting.
This seminar first introduces automated transcription (assuming no prior knowledge) and provides an overview of different speech-to-text apps, before addressing common FAQs, important considerations, and recommendations for “best practice” use. There will be the opportunity for discussion where attendees can pose questions, highlight critical issues, and share different experiences or concerns of using transcription software in different contexts.
You can access Caitlin’s 3-part blog post series on automated transcription for qualitative research here: 1) Introducing automated transcription for qualitative research; 2) A tutorial; 3) Ethical, privacy, security, and GDPR considerations.
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