Using automated syllable counting to detect missing information in speech transcripts from clinical settings
AuthorDiaz-Asper, Marama; Holmlund, Terje Bektesevic; Chandler, Chelsea; Diaz-Asper, Catherine; Foltz, Peter W.; Cohen, Alex S.; Elvevåg, Brita
Speech 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.
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;315
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