Speech based Depression Severity Level Classification Using a Multi-Stage Dilated CNN-LSTM Model

• URL: http://arxiv.org/abs/2104.04195v1
• Date: Fri, 9 Apr 2021 05:10:08 GMT
• Title: Speech based Depression Severity Level Classification Using a Multi-Stage Dilated CNN-LSTM Model
• Authors: Nadee Seneviratne, Carol Espy-Wilson
• Abstract summary: We formulate the depression classification task as a severity level classification problem to provide more granularity to the classification outcomes. We use articulatory coordination features (ACFs) developed to capture the changes of neuromotor coordination that happens as a result of psychomotor slowing.
• Score: 5.419077350924331
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Speech based depression classification has gained immense popularity over the recent years. However, most of the classification studies have focused on binary classification to distinguish depressed subjects from non-depressed subjects. In this paper, we formulate the depression classification task as a severity level classification problem to provide more granularity to the classification outcomes. We use articulatory coordination features (ACFs) developed to capture the changes of neuromotor coordination that happens as a result of psychomotor slowing, a necessary feature of Major Depressive Disorder. The ACFs derived from the vocal tract variables (TVs) are used to train a dilated Convolutional Neural Network based depression classification model to obtain segment-level predictions. Then, we propose a Recurrent Neural Network based approach to obtain session-level predictions from segment-level predictions. We show that strengths of the segment-wise classifier are amplified when a session-wise classifier is trained on embeddings obtained from it. The model trained on ACFs derived from TVs show relative improvement of 27.47% in Unweighted Average Recall (UAR) at the session-level classification task, compared to the ACFs derived from Mel Frequency Cepstral Coefficients (MFCCs).

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