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dc.contributor.authorDiaz-Asper, Marama
dc.contributor.authorHolmlund, Terje Bektesevic
dc.contributor.authorChandler, Chelsea
dc.contributor.authorDiaz-Asper, Catherine
dc.contributor.authorFoltz, Peter W.
dc.contributor.authorCohen, Alex S.
dc.contributor.authorElvevåg, Brita
dc.description.abstractSpeech rate and quantity reflect clinical state; thus automated transcription holds potential clinical applications. We describe two datasets where recording quality and speaker characteristics affected transcription accuracy. Transcripts of low-quality recordings omitted significant portions of speech. An automated syllable counter estimated actual speech output and quantified the amount of missing information. The efficacy of this method differed by audio quality: the correlation between missing syllables and word error rate was only significant when quality was low. Automatically counting syllables could be useful to measure and flag transcription omissions in clinical contexts where speaker characteristics and recording quality are problematic.en_US
dc.identifier.citationDiaz-Asper, Holmlund, Chandler, Diaz-Asper, Foltz, Cohen, Elvevåg. Using automated syllable counting to detect missing information in speech transcripts from clinical settings. Psychiatry Research. 2022;315en_US
dc.identifier.cristinIDFRIDAID 2058268
dc.relation.journalPsychiatry Research
dc.rights.holderCopyright 2022 The Author(s)en_US
dc.rightsAttribution 4.0 International (CC BY 4.0)en_US
dc.titleUsing automated syllable counting to detect missing information in speech transcripts from clinical settingsen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US

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Attribution 4.0 International (CC BY 4.0)
Except where otherwise noted, this item's license is described as Attribution 4.0 International (CC BY 4.0)