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

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 spe...

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Detalles Bibliográficos
Autores principales: Diaz-Asper, Marama, Holmlund, Terje B., Chandler, Chelsea, Diaz-Asper, Catherine, Foltz, Peter W., Cohen, Alex S., Elvevåg, Brita
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9378537/
https://www.ncbi.nlm.nih.gov/pubmed/35839638
http://dx.doi.org/10.1016/j.psychres.2022.114712

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9378537/
https://www.ncbi.nlm.nih.gov/pubmed/35839638
http://dx.doi.org/10.1016/j.psychres.2022.114712

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