Deficits in nonverbal vocal expression (e.g., blunted vocal affect, alogia) are a hallmark of schizophrenia and are a focus of the Research Domain Criteria initiative from the National Institute of Mental Health. Results from studies using symptom rating scales suggest these deficits are profound; on the order of four to six standard deviations. To complement this endeavor, we conducted a meta-analysis of studies employing objective analysis of natural speech in patients with schizophrenia and nonpsychiatric controls. Thirteen studies, collectively including 480 patients with schizophrenia and 326 nonpsychiatric controls, were identified. There was considerable variability across studies in which aspects of vocal communication were examined and in the magnitudes of deficit. Overall, speech production (reflecting alogia) was impaired at a large effects size level (d = −.80; k = 13), whereas speech variability (reflecting blunted affect) was much more modest (d = −.36; k = 2). Regarding the former, this was largely driven by measures of pause behavior, as opposed to other aspects of speech (e.g., number of words/utterances). On the other hand, ratings of negative symptoms across these studies suggested profound group differences (d = 3.54; k = 4). These data suggest that only certain aspects of vocal expression are affected in schizophrenia, and highlight major discrepancies between symptom rating and objective-based measures. The discussion centers on advancing objective analysis for understanding vocal expression in schizophrenia and for identifying and defining more homogenous patient subsets for study.

Abnormalities in nonverbal communication are a hallmark of schizophrenia. Results from studies using symptom rating scales suggest that these abnormalities are profound (i.e., 3–5 standard deviations) and occur across virtually every channel of vocal expression. Computerized acoustic analytic technologies, employed to overcome practical and psychometric limitations with symptom rating scales, have found much more benign and isolated abnormalities. In order to better understand vocal deficits in schizophrenia and to advance acoustic analytic technologies for clinical and research applications, we examined archived speech samples from five separate studies, each employing different speaking tasks (patient N = 309; control N = 117). We sought to: a) employ Principal Component Analysis (PCA) to identify independent vocal expression measures from a large set of variables, b) quantify how patients with schizophrenia are abnormal with respect to these variables, c) evaluate the impact of demographic and contextual factors (e.g., study site, speaking task), and d) examine the relationship between clinically–rated psychiatric symptoms and vocal variables. PCA identified seven independent markers of vocal expression. Most of these vocal variables varied considerably as a function of context and many were associated with demographic factors. After controlling for context and demographics, there were no meaningful differences in vocal expression between patients and controls. Within patients, vocal variables were associated with a range of psychiatric symptoms – though only pause length was significantly associated with clinically-rated negative symptoms. The discussion centers on explaining the apparent discordance between clinical and computerized speech measures.

Analysis of vocal expression is a critical endeavor for psychological and clinical sciences, and is an increasingly popular application for computer-human interfaces. Despite this, and advances in the efficiency, affordability and sophistication of vocal analytic technologies, there is considerable variability across studies regarding what aspects of vocal expression are studied. Vocal signals can be quantified in a myriad of ways and its underlying structure, at least with respect to “macroscopic” measures from extended speech, is presently unclear. To address this issue, we evaluated psychometric properties, notably structural and construct validity, of a systematically-defined set of global vocal features. Our analytic strategy focused on: a) identifying redundant variables among this set, b) employing Principal Components Analysis (PCA) to identify non-overlapping domains of vocal expression, c) examining the degree to which vocal variables modulate as a function of changes in speech task, and d) evaluating the relationship between vocal variables and cognitive (i.e., verbal fluency) and clinical (i.e., depression, anxiety, hostility) variables. Spontaneous speech samples from 11 independent studies of young adults (> 60 seconds in length), employing one of three different speaking tasks, were examined (N = 1350). Confounding variables (i.e., sex, ethnicity) were statistically controlled for. The PCA identified six distinct domains of vocal expression. Collectively, vocal expression (defined in terms of these domains) modulated as a function of speech task and was related to cognitive and clinical variables. These findings provide empirically-grounded implications for the study of vocal expression in psychological and clinical sciences.

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