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Automatic speech-to-text transcription: evidence from a smartphone survey with voice answers.

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Titel: Automatic speech-to-text transcription: evidence from a smartphone survey with voice answers.
Autoren: Höhne, Jan Karem, Lenzner, Timo, Claassen, Joshua
Quelle: International Journal of Social Research Methodology; Sep2025, Vol. 28 Issue 5, p625-632, 8p
Schlagwörter: AUTOMATIC speech recognition, VOICE recognition software, PHONETICS, TRANSCRIPTION (Linguistics)
Geografische Kategorien: GERMANY
Firma/Körperschaft: GOOGLE Inc.
Abstract: Advances in information and communication technology, coupled with a smartphone increase in web surveys, provide new avenues for collecting answers from respondents. Specifically, the microphones of smartphones facilitate the collection of voice instead of text answers to open questions. Speech-to-text transcriptions through Automatic Speech Recognition (ASR) systems pose an efficient way to make voice answers accessible to text-as-data methods. However, there is little evidence on the transcription performance of ASR systems when it comes to voice answers. We therefore investigate the performance of two leading ASR systems – Google's Cloud Speech-to-Text API and OpenAI's Whisper – using voice answers to two open questions administered in a smartphone survey in Germany. The results indicate that Whisper produces more accurate transcriptions than Google's API. Both systems produce similar errors, but these errors are more common for the Google API. However, the Google API is faster than both Whisper and human transcribers. [ABSTRACT FROM AUTHOR]
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Datenbank: Biomedical Index
Beschreibung
Abstract:Advances in information and communication technology, coupled with a smartphone increase in web surveys, provide new avenues for collecting answers from respondents. Specifically, the microphones of smartphones facilitate the collection of voice instead of text answers to open questions. Speech-to-text transcriptions through Automatic Speech Recognition (ASR) systems pose an efficient way to make voice answers accessible to text-as-data methods. However, there is little evidence on the transcription performance of ASR systems when it comes to voice answers. We therefore investigate the performance of two leading ASR systems – Google's Cloud Speech-to-Text API and OpenAI's Whisper – using voice answers to two open questions administered in a smartphone survey in Germany. The results indicate that Whisper produces more accurate transcriptions than Google's API. Both systems produce similar errors, but these errors are more common for the Google API. However, the Google API is faster than both Whisper and human transcribers. [ABSTRACT FROM AUTHOR]
ISSN:13645579
DOI:10.1080/13645579.2024.2443633