Gargi Misra
ABSTRACT
Depression is a prevalent psychiatric condition that has to be identified and treated right away. It may cause suicidal ideation in extreme instances. The requirement for creating an efficient audio-based automated depression identification system has recently piqued the fascination of researchers. The bulk of studies conducted so far incorporates a broad range of expertly created audio elements for a depression diagnosis. This expands feature space and causes a high-dimensionality problem which complicates pattern identification and increases the chance of data imbalance. This paper suggests a deep learning autoencoder-based method to retrieve pertinent and condensed features from speech signals in order to more precisely diagnose mental illness. The performance and efficacy of the suggested approach are evaluated on the DAIC-WoZ dataset and compared the results with other noteworthy machine learning algorithms. According to the findings, this technique works better than existing audio-based depression detection models when used with an SVM classifier resulting in an accuracy of 97% for diagnosing depression.
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Index Terms
- Deep Learning Technique to Diagnose Depression in Audio
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