Automated speech analysis for psychosis detection: a systematic comparison of task efficiency and feature groups

Abstract This study investigates the effectiveness of automatic speech analysis in differentiating between patients with psychotic disorders and healthy control individuals with low or high levels of schizotypy within a German-speaking cohort. A range of acoustic and linguistic features was extracted from audio recordings and transcriptions obtained through structured interviews, open discourse, and picture description tasks, in which each participant described at least six pictures. These features served as input to train Random Forest classifiers, with linguistic features achieving consistent area under the receiver operating characteristic curve values of around 0.8–0.9 across all interview formats. In contrast, acoustic features performed significantly worse, particularly in the picture description task. Using linguistic features, we showed that increasing the number of described pictures significantly improved model accuracy, with performance plateauing after the combination of three pictures. Subsequent feature importance analysis revealed that patients produced simpler language, shorter sentences, less speech overall, and exhibited lower semantic coherence compared to healthy controls. These findings highlight the potential of automatic linguistic analysis as a scalable and objective tool for the detection of psychotic disorders in German-speaking populations.