Clinical investigation of speech signal features among patients with schizophrenia.

Abstract

BackgroundA new area of interest in the search for biomarkers for schizophrenia is the study of the acoustic parameters of speech called 'speech signal features'. Several of these features have been shown to be related to emotional responsiveness, a characteristic that is notably restricted in patients with schizophrenia, particularly those with prominent negative symptoms. AimAssess the relationship of selected acoustic parameters of speech to the severity of clinical symptoms in patients with chronic schizophrenia and compare these characteristics between patients and matched healthy controls. MethodsTen speech signal features-six prosody features, formant bandwidth and amplitude, and two spectral features-were assessed using 15-minute speech samples obtained by smartphone from 26 inpatients with chronic schizophrenia (at enrollment and 1 week later) and from 30 healthy controls (at enrollment only). Clinical symptoms of the patients were also assessed at baseline and 1 week later using the Positive and Negative Syndrome Scale, the Scale for the Assessment of Negative Symptoms, and the Clinical Global Impression-Schizophrenia scale. ResultsIn the patient group the symptoms were stable over the 1-week interval and the 1-week test-retest reliability of the 10 speech features was good (intraclass correlation coefficients [ICC] ranging from 0.55 to 0.88). Comparison of the speech features between patients and controls found no significant differences in the six prosody features or in the formant bandwidth and amplitude features, but the two spectral features were different: the Mel-frequency cepstral coefficient (MFCC) scores were significantly lower in the patient group than in the control group, and the linear prediction coding (LPC) scores were significantly higher in the patient group than in the control group. Within the patient group, 10 of the 170 associations between the 10 speech features considered and the 17 clinical parameters considered were statistically significant at the p<0.05 level. ConclusionsThis study provides some support for the potential value of speech signal features as indicators (i.e., biomarkers) of the severity of negative symptoms in schizophrenia, but more detailed studies using larger samples of more diverse patients that are followed over time will be needed before the potential utility of such acoustic parameters of speech can be fully assessed.