Automatic Transcription of English and German Qualitative Interviews

Abstract

Recording and transcribing interviews in qualitative social research is a vital but time-consuming and resource-intensive task. To tackle this challenge, researchers have explored various alternative approaches; automatic transcription utilising speech recognition algorithms has emerged as a promising solution. The question of whether automated transcripts can match the quality of transcripts produced by humans remains unanswered. In this paper we systematically compare multiple automatic transcription tools: Amberscript, Dragon, F4x, Happy Scribe, NVivo, Sonix, Trint, Otter, and Whisper. We evaluate aspects of data protection, accuracy, time efficiency, and costs for an English and a German interview. Based on the analysis, we conclude that Whisper performs best overall and that similar local-automatic transcription tools are likely to become more relevant. For any type of transcription, we recommend reviewing the text to ensure accuracy. We hope to shed light on the effectiveness of automatic transcription services and provide a comparative frame for others interested in automatic transcription.

Keywords

• qualitative interviews
• transcription standard
• methods
• automatisation
• data protection
• accuracy