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Speech based Depression Severity Level Classification Using a Multi-Stage Dilated CNN-LSTM Model
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 neuromotordoi:10.48550/arxiv.2104.04195 fatcat:rynvy3chhbe2hojpru45xsyqhq