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Psychomotor retardation associated with depression has been linked withtangible differences in vowel production. This paper investigates aknowledge-driven machine learning (ML) method that integrates spectrotemporalinformation of speech at the vowel-level to identify the depression. Low-levelspeech descriptors are learned by a convolutional neural network (CNN) that istrained for vowel classification. The temporal evolution of those low-leveldescriptors is modeled at the high-level within and across utterances via along short-term memory (LSTM) model that takes the final depression decision. Amodified version of the Local Interpretable Model-agnostic Explanations (LIME)is further used to identify the impact of the low-level spectrotemporal vowelvariation on the decisions and observe the high-level temporal change of thedepression likelihood. The proposed method outperforms baselines that model thespectrotemporal information in speech without integrating the vowel-basedinformation, as well as ML models trained with conventional prosodic andspectrotemporal features. The conducted explainability analysis indicates thatspectrotemporal information corresponding to non-vowel segments less importantthan the vowel-based information. Explainability of the high-level informationcapturing the segment-by-segment decisions is further inspected forparticipants with and without depression. The findings from this work canprovide the foundation toward knowledge-driven interpretable decision-supportsystems that can assist clinicians to better understand fine-grain temporalchanges in speech data, ultimately augmenting mental health diagnosis and care.

Toward Knowledge-Driven Speech-Based Models of Depression: Leveraging Spectrotemporal Variations in Speech Vowels