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Speech and Text Processing for Major Depressive Disorder Detection

Major Depressive Disorder (MDD) is a common mental health issue these days. Its early diagnostic is vital to avoid bigger con-sequences and provide an appropriate treatment. Speech and utterance’s transcription of patients’ interviews contain useful information sources for the automatic screening of MDD. In this sense, speech- and text-based systems are proposed in this paper, using the DAIC-WOZ dataset as experimental frame-work. The speech-based one is a Sequence-to-Sequence (S2S) model with a local attention mechanism. The text-based one is based on GloVe features and a Convolutional Neural Network as classifier. A description of some of the more relevant results achieved by other research publications on DAIC-WOZ are described as well. The goal is to provide a better understanding of the context of our systems results. In general, the S2S architecture provides mostly better results than previous speech-based systems. The GloVe-CNN system shows even a better performance, leading to the idea that text is a more suitable information source for the detection of MDD when it is manually developed. However, to automatically obtain high quality transcriptions is not a straightforward task, which makes necessary the development of effective speech-based systems as the pre-sented in this research work.