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Key features of mental illnesses are reflected in speech. Our researchfocuses on designing a multimodal deep learning structure that automaticallyextracts salient features from recorded speech samples for predicting variousmental disorders including depression, bipolar, and schizophrenia. We adopt avariety of pre-trained models to extract embeddings from both audio and textsegments. We use several state-of-the-art embedding techniques including BERT,FastText, and Doc2VecC for the text representation learning and WaveNet andVGG-ish models for audio encoding. We also leverage huge auxiliaryemotion-labeled text and audio corpora to train emotion-specific embeddings anduse transfer learning in order to address the problem of insufficient annotatedmultimodal data available. All these embeddings are then combined into a jointrepresentation in a multimodal fusion layer and finally a recurrent neuralnetwork is used to predict the mental disorder. Our results show that mentaldisorders can be predicted with acceptable accuracy through multimodal analysisof clinical interviews.

Multimodal Deep Learning for Mental Disorders Prediction from Audio Speech Samples