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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...
| Autores principales: | , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
2022
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| Materias: | |
| 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 |