Automatic analysis of speech representations to assess psychological distress.

Background: Current mental health diagnostic methods are limited by subjective clinical interpretation. Automatic speech analysis is a promising technology for objective assessment. Objective: To evaluate and compare different speech-based representations (acoustic, phonetic, and time-frequency) and deep learning-based embeddings for discriminating symptoms associated with psychological distress. Methods: A secondary analysis of the Distress Analysis Interview Corpus (DAIC-WOZ) was conducted using recordings from 125 participants (3,069 responses). Speech representations included phonation, articulation, and prosody features extracted with DisVoice; phonetic features extracted with Phonet; time-frequency representations derived from Mexican hat wavelets; and deep embeddings extracted with the multilingual Wav2Vec 2.0 model XLSR-53. Two classification strategies were addressed at the response and participant levels using a Fully Connected Neural Network (FCNN) and a Support Vector Machine (SVM), respectively. Results: Prosody at the participant level achieved the highest mean performance (F1-score 0.67 Conclusion: Participant-level analysis provided more robust and consistent discriminative patterns than response-level approaches. Prosody and phonation achieved the best performance across speech representations, while phonetic, time-frequency, and deep speech representations did not outperform the best acoustic baseline. These findings suggest that, within the evaluated experimental setting, the aggregation strategy appears to have a stronger influence on performance than increasing representational complexity.