Applications of Speech Analysis in Psychiatry

Background/Objectives: Psychiatry lacks the objective clinical tests routinely used in other specializations. Novel computerized methods to characterize complex behaviors such as speech could be used to identify and predict psychiatric illness in individuals. AIMS: In this proof-of-principle study, our aim was to test automated speech analyses combined with Machine Learning to predict later psychosis onset in youths at clinical high-risk (CHR) for psychosis. Methods: Thirty-four CHR youths (11 females) had baseline interviews and were assessed quarterly for up to 2.5 years; five transitioned to psychosis. Using automated analysis, transcripts of interviews were evaluated for semantic and syntactic features predicting later psychosis onset. Speech features were fed into a convex hull classification algorithm with leave-one-subject-out cross-validation to assess their predictive value for psychosis outcome. The canonical correlation between the speech features and prodromal symptom ratings was computed. Results: Derived speech features included a Latent Semantic Analysis measure of semantic coherence and two syntactic markers of speech complexity: maximum phrase length and use of determiners (e.g., which). These speech features predicted later psychosis development with 100% accuracy, outperforming classification from clinical interviews. Speech features were significantly correlated with prodromal symptoms. Conclusions: Findings support the utility of automated speech analysis to measure subtle, clinically relevant mental state changes in emergent psychosis. Recent developments in computer science, including natural language processing, could provide the foundation for future development of objective clinical tests for psychiatry.

In this article we report meta-analyses of the relation of attributional styles to depression. In 104 studies involving nearly 15,000 subjects, several attributional patterns had reliable associations with depression scores. For negative events, attributions to internal, stable, and global causes had a reliable and significant association with depression. Studies in which the attribution factors of ability and luck were measured also showed a reliable association with depression. For positive events, attributions to external, unstable, and specific causes were associated with depression. Ability and luck attribution factors for positive events were also associated with depression. The relations for positive events, however, were weaker than the corresponding ones for negative events. In general, these patterns of relations were independent of a number of potential mediators suggested by authors in this literature, including the type of subject studied (psychiatric vs. college student), the type of event about which the attribution is made (real vs. simulated), the depression measure used, or the publication status of the research report. These conclusions are compared with those of other reviews. Implications for attributional models of depression are discussed.

The authors compared measurement-based care with standard treatment in major depression.