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

Author:

Diaz-Asper Marama,Holmlund Terje B.,Chandler Chelsea,Diaz-Asper Catherine,Foltz Peter W.,Cohen Alex S.,Elvevåg BritaORCID

Publisher

Elsevier BV

Subject

Biological Psychiatry,Psychiatry and Mental health

Reference15 articles.

1. A statistical analysis on the impact of noise on MFCC features for speech recognition;Bhattacharjee,2016

2. Boersma, P., & Weenink, D. (2020). Praat: doing phonetics by computer [Computer program]. Version 6.1.16, retrieved 6 July 2020 from http://www.praat.org/.

3. Overcoming the bottleneck in traditional assessments of verbal memory: modeling human ratings and classifying clinical group membership;Chandler,2019

4. Disturbing the rhythm of thought: speech pausing patterns in schizophrenia, with and without formal thought disorder;Çokal;PLoS ONE,2019

5. Praat script to detect syllable nuclei and measure speech rate automatically;de Jong;Behav. Res. Methods,2009

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