Emotional Vietnamese Speech-Based Depression Diagnosis Using Dynamic Attention Mechanism

TL;DR

This paper introduces a novel deep learning model combining Dynamic-CBAM and Attention-GRU to analyze Vietnamese speech signals for depression detection, achieving high accuracy and aiding early diagnosis.

Contribution

It proposes a new attention-based neural network architecture specifically designed for Vietnamese speech emotion analysis for depression diagnosis.

Findings

Achieved 0.87 UA and 0.86 WA accuracy on VNEMOS dataset.

Demonstrated effectiveness of Dynamic-CBAM in speech emotion classification.

Provided open-source code for reproducibility.

Abstract

Major depressive disorder is a prevalent and serious mental health condition that negatively impacts your emotions, thoughts, actions, and overall perception of the world. It is complicated to determine whether a person is depressed due to the symptoms of depression not apparent. However, their voice can be one of the factor from which we can acknowledge signs of depression. People who are depressed express discomfort, sadness and they may speak slowly, trembly, and lose emotion in their voices. In this study, we proposed the Dynamic Convolutional Block Attention Module (Dynamic-CBAM) to utilized with in an Attention-GRU Network to classify the emotions by analyzing the audio signal of humans. Based on the results, we can diagnose which patients are depressed or prone to depression then so that treatment and prevention can be started as soon as possible. The research delves into the intricate computational steps involved in implementing a Attention-GRU deep learning architecture. Through experimentation, the model has achieved an impressive recognition with Unweighted Accuracy (UA) rate of 0.87 and 0.86 Weighted Accuracy (WA) rate and F1 rate of 0.87 in the VNEMOS dataset. Training code is released in https://github.com/fiyud/Emotional-Vietnamese-Speech-Based-Depression-Diagnosis-Using-Dynamic-Attention-Mechanism