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Speech Recognition Errors in ASR Engines and Their Impact on Linguistic Analysis in Psychotherapies

  • Modern intervention planning in psychotherapies may benefit from predicting process relevant psychotherapy constructs by automated speech analysis. One essential step is the extraction of relevant linguistic speech markers by ASR engines, which because of highly sensible data, work offline. We analyze transcription errors from NeMo, Whisper, and Wav2Vec2.0, focusing on their impact on linguistic markers usually requiring high quality transcripts. By utilizing part-of-speech tagging, we examine error occurrences among different word types. The Linguistic Inquiry and Word Count (LIWC) software aids in extracting markers. We highlight challenges in transcribing spontaneous speech, prevalent in therapy, and compare results with the Mozilla CommonVoice dataset, which features read speech.

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Metadaten
Author:Martha Schubert, Yamini Sinha, Julia Krüger, Ingo Siegert
URN:urn:nbn:de:bvb:898-opus4-70999
DOI:https://doi.org/10.35096/othr/pub-7099
ISBN:978-3-95908-325-6
Parent Title (German):Elektronische Sprachsignalverarbeitung 2024, Tagungsband der 35. Konferenz, Regensburg, 6.-8. März 2024
Publisher:TUDpress
Place of publication:Dresden
Editor:Timo Baumann
Document Type:conference proceeding (article)
Language:English
Year of first Publication:2024
Publishing Institution:Ostbayerische Technische Hochschule Regensburg
Release Date:2024/03/08
First Page:203
Last Page:210
Andere Schriftenreihe:Studientexte zur Sprachkommunikation ; 107
Institutes:Fakultät Informatik und Mathematik
research focus:Information und Kommunikation
Licence (German):Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG