Transcribing and coding voice answers obtained in web surveys: comparing three leading automatic speech recognition tools

Data de publicació electrònica: 22-03-2026

Detalles Bibliográficos
Autores: Revilla, Melanie, Ochoa Gómez, Carlos, Höhne, Jan Karem, Couper, Mick P.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2026
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:dnet:recercat____::7952bfebcec8e283aec0ee3ec8975b7a
Acceso en línea:https://hdl.handle.net/10230/73286
http://dx.doi.org/10.1093/jssam/smaf028
Access Level:acceso abierto
Palabra clave:Automatic speech recognition
Google&apos
s cloud speech-to-text API
GPT-4o
Large language model
OpenAI whisper
Voice answer transcription
Vosk
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oai_identifier_str oai:dnet:recercat____::7952bfebcec8e283aec0ee3ec8975b7a
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spelling Transcribing and coding voice answers obtained in web surveys: comparing three leading automatic speech recognition toolsRevilla, MelanieOchoa Gómez, CarlosHöhne, Jan KaremCouper, Mick P.Automatic speech recognitionGoogle&aposs cloud speech-to-text APIGPT-4oLarge language modelOpenAI whisperVoice answer transcriptionVoskData de publicació electrònica: 22-03-2026With the rise of smartphone use in web surveys, voice or oral answers have become a promising methodology for collecting rich data. Voice answers present both opportunities and challenges. This study addresses two of these challenges-labor-intensive manual transcription and coding of responses. We compare the transcription performance of three leading Automatic Speech Recognition (ASR) tools-Google Cloud Speech-to-Text API, OpenAI Whisper, and Vosk-using voice answers collected from an open-ended question on nursing home transparency that was administered in an opt-in online panel in Spain. Additionally, we evaluate the efficiency and quality of coding these transcriptions using human coders and GPT-4o, a Large Language Model (LLM) developed by OpenAI. We found that each of the ASR tools has distinct merits and limits. Google sometimes fails to provide transcriptions, Whisper produces hallucinations (false transcriptions), and Vosk has clarity issues and high rates of incorrect words. Human and LLM-based coding also differ significantly. Thus, we recommend using several ASR tools for voice answer transcription and implementing human as well as LLM-based coding, as the latter offers additional information at minimal added cost.Oxford University Press2026202620262026info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/10230/73286http://dx.doi.org/10.1093/jssam/smaf028https://hdl.handle.net/10230/73286reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésJournal of Survey Statistics and Methodology. 2026 Mar 22© The Author(s) 2026. Published by Oxford University Press on behalf of the American Association for Public Opinion Research. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited.Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttps://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:dnet:recercat____::7952bfebcec8e283aec0ee3ec8975b7a2026-05-29T05:05:01Z
dc.title.none.fl_str_mv Transcribing and coding voice answers obtained in web surveys: comparing three leading automatic speech recognition tools
title Transcribing and coding voice answers obtained in web surveys: comparing three leading automatic speech recognition tools
spellingShingle Transcribing and coding voice answers obtained in web surveys: comparing three leading automatic speech recognition tools
Revilla, Melanie
Automatic speech recognition
Google&apos
s cloud speech-to-text API
GPT-4o
Large language model
OpenAI whisper
Voice answer transcription
Vosk
title_short Transcribing and coding voice answers obtained in web surveys: comparing three leading automatic speech recognition tools
title_full Transcribing and coding voice answers obtained in web surveys: comparing three leading automatic speech recognition tools
title_fullStr Transcribing and coding voice answers obtained in web surveys: comparing three leading automatic speech recognition tools
title_full_unstemmed Transcribing and coding voice answers obtained in web surveys: comparing three leading automatic speech recognition tools
title_sort Transcribing and coding voice answers obtained in web surveys: comparing three leading automatic speech recognition tools
dc.creator.none.fl_str_mv Revilla, Melanie
Ochoa Gómez, Carlos
Höhne, Jan Karem
Couper, Mick P.
author Revilla, Melanie
author_facet Revilla, Melanie
Ochoa Gómez, Carlos
Höhne, Jan Karem
Couper, Mick P.
author_role author
author2 Ochoa Gómez, Carlos
Höhne, Jan Karem
Couper, Mick P.
author2_role author
author
author
dc.subject.none.fl_str_mv Automatic speech recognition
Google&apos
s cloud speech-to-text API
GPT-4o
Large language model
OpenAI whisper
Voice answer transcription
Vosk
topic Automatic speech recognition
Google&apos
s cloud speech-to-text API
GPT-4o
Large language model
OpenAI whisper
Voice answer transcription
Vosk
description Data de publicació electrònica: 22-03-2026
publishDate 2026
dc.date.none.fl_str_mv 2026
2026
2026
2026
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/10230/73286
http://dx.doi.org/10.1093/jssam/smaf028
https://hdl.handle.net/10230/73286
url https://hdl.handle.net/10230/73286
http://dx.doi.org/10.1093/jssam/smaf028
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Journal of Survey Statistics and Methodology. 2026 Mar 22
dc.rights.none.fl_str_mv Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Oxford University Press
publisher.none.fl_str_mv Oxford University Press
dc.source.none.fl_str_mv reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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