Automatic Depression Classification Based on Affective Read Sentences: Opportunities for Text-Dependent Analysis

In the future, automatic speech-based analysis of mental health could become widely available to help augment conventional healthcare evaluation methods. For speech-based patient evaluations of this kind, protocol design is a key consideration. Read speech provides an advantage over other verbal modes (e.g. automatic, spontaneous) by providing a clinically stable and repeatable protocol. Further, text-dependent speech helps to reduce phonetic variability and delivers controllable linguistic/affective stimuli, therefore allowing more precise analysis of recorded stimuli deviations. The purpose of this study is to investigate speech disfluency behaviors in nondepressed/depressed speakers using read aloud text containing constrained affective-linguistic criteria. Herein, using the Black Dog Institute Affective Sentences (BDAS) corpus, analysis demonstrates statistically significant feature differences in speech disfluencies, whereby when compared to non-depressed speakers, depressed speakers show relatively higher recorded frequencies of hesitations (55% increase) and speech errors (71% increase). Our study examines both manually and automatically labeled speech disfluency features, demonstrating that detailed disfluency analysis leads to considerable gains, of up to 100% in absolute depression classification accuracy, especially with affective considerations, when compared with the affect-agnostic acoustic baseline (65%).