Using automated syllable counting to detect missing information in speech transcripts from clinical settings

Authors

Marama Diaz-Asper, Washington International School, Washington DC, United States.
Terje B. Holmlund, Department of Clinical Medicine, University of Tromsø - The Arctic University of Norway, Tromsø, Norway.
Chelsea Chandler, Department of Computer Science, University of Colorado Boulder, CO, United States.
Catherine Diaz-Asper, Department of Psychology, Marymount University, Arlington, VA, United States.
Peter W. Foltz, Institute of Cognitive Science, University of Colorado Boulder, CO, United States.
Alex S. Cohen, Department of Psychology, Louisiana State University, LA, United States.
Brita Elvevåg, Department of Clinical Medicine, University of Tromsø - The Arctic University of Norway, Tromsø, Norway; Norwegian Center for eHealth Research, University Hospital of North Norway, Tromsø, Norway. Electronic address: brita.elvevag@uit.no.

Document Type

Article

Publication Date

9-1-2022

Abstract

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.

Publication Source (Journal or Book title)

Psychiatry research

First Page

114712

Recommended Citation

Diaz-Asper, M., Holmlund, T. B., Chandler, C., Diaz-Asper, C., Foltz, P. W., Cohen, A. S., & Elvevåg, B. (2022). Using automated syllable counting to detect missing information in speech transcripts from clinical settings. Psychiatry research, 315, 114712. https://doi.org/10.1016/j.psychres.2022.114712