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Using machine learning of computerized vocal expression to measure blunted vocal affect and alogia

Psychology

Using machine learning of computerized vocal expression to measure blunted vocal affect and alogia

A. S. Cohen, C. R. Cox, et al.

This groundbreaking study by Alex S. Cohen and colleagues delves into machine learning's potential to model blunted vocal affect and alogia with impressive accuracy. The findings reveal correlations with cognitive performance and offer insights into digital phenotyping for serious mental illness, raising intriguing questions about the nature of vocal expression in schizophrenia.

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Playback language: English
Abstract
This study investigated whether machine learning (ML) could accurately model clinically rated blunted vocal affect (BvA) and alogia using a large acoustic feature set from two speaking tasks. High accuracy (>90%) was achieved, improving when tasks were analyzed separately. ML scores correlated with poor cognitive performance and social functioning, and were higher in schizophrenia patients. However, the most predictive vocal features were not those considered central to BvA/alogia definitions. Implications for digital phenotyping in serious mental illness are discussed.
Publisher
npj Schizophrenia
Published On
Sep 25, 2020
Authors
Alex S. Cohen, Christopher R. Cox, Thanh P. Le, Tovah Cowan, Michael D. Masucci, Gregory P. Strauss, Brian Kirkpatrick
Tags
machine learning
blunted vocal affect
alogia
schizophrenia
digital phenotyping
acoustic features

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