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Can the Acoustic Analysis of Expressive Prosody Discriminate Schizophrenia?

Published online by Cambridge University Press:  02 November 2015

Francisco Martínez-Sánchez ,
José Antonio Muela-Martínez ,
Pedro Cortés-Soto ,
Juan José García Meilán ,
Juan Antonio Vera Ferrándiz ,
Amaro Egea Caparrós  and
Isabel María Pujante Valverde
Francisco Martínez-Sánchez*
Affiliation:
Universidad de Murcia (Spain)
José Antonio Muela-Martínez
Affiliation:
Universidad de Jaén (Spain)
Pedro Cortés-Soto
Affiliation:
Universidad de Jaén (Spain)
Juan José García Meilán
Affiliation:
Universidad de Salamanca (Spain)
Juan Antonio Vera Ferrándiz
Affiliation:
Universidad de Murcia (Spain)
Amaro Egea Caparrós
Affiliation:
Universidad de Murcia (Spain)
Isabel María Pujante Valverde
Affiliation:
Universidad de Murcia (Spain)
*
*Correspondence concerning this article should be addressed to Francisco Martínez-Sánchez. Dpto. de Psicología Básica y Metodología. Facultad de Psicología. Universidad de Murcia. Campus Universitario de Espinardo. 30100. Murcia (Spain). E-mail: franms@um.es
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Abstract

Emotional states, attitudes and intentions are often conveyed by modulations in the tone of voice. Impaired recognition of emotions from a tone of voice (receptive prosody) has been described as characteristic symptoms of schizophrenia. However, the ability to express non-verbal information in speech (expressive prosody) has been understudied. This paper describes a useful technique for quantifying the degree of expressive prosody deficits in schizophrenia, using a semi-automatic method, and evaluates this method’s ability to discriminate between patient and control groups. Forty-five medicated patients with a diagnosis of schizophrenia were matched with thirty-five healthy comparison subjects. Production of expressive prosodic speech was analyzed using variation in fundamental frequency (F0) measures on an emotionally neutral reading task. Results revealed that patients with schizophrenia exhibited significantly more pauses (p < .001), were slower (p < .001), and showed less pitch variability in speech (p < .05) and fewer variations in syllable timing (p < .001) than control subjects. These features have been associated with «flat» speech prosody. Signal processing algorithms applied to speech were shown to be capable of discriminating between patients and controls with an accuracy of 93.8%. These speech parameters may have a diagnostic and prognosis value and therefore could be used as a dependent measure in clinical trials.

Keywords

Schizophreniaflat affectexpressive prosodyspeech analysis
Type
Research Article
Information
The Spanish Journal of Psychology , Volume 18 , 2015 , E86
Copyright
Copyright © Universidad Complutense de Madrid and Colegio Oficial de Psicólogos de Madrid 2015 

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