Speech Emotion Recognition using Supervised Deep Recurrent System for Mental Health Monitoring

TL;DR

This paper presents a deep learning model combining gated recurrent and convolutional neural networks to recognize emotions from speech, aiming to enhance virtual assistants and support mental health monitoring, especially during pandemic-related mental health issues.

Contribution

It introduces a novel deep learning architecture that integrates gated recurrent and convolutional neural networks for speech emotion recognition, advancing mental health monitoring capabilities.

Findings

Improved accuracy in emotion recognition from speech.

Enhanced virtual assistant responsiveness to emotional cues.

Potential for early mental health issue detection.

Abstract

Understanding human behavior and monitoring mental health are essential to maintaining the community and society's safety. As there has been an increase in mental health problems during the COVID-19 pandemic due to uncontrolled mental health, early detection of mental issues is crucial. Nowadays, the usage of Intelligent Virtual Personal Assistants (IVA) has increased worldwide. Individuals use their voices to control these devices to fulfill requests and acquire different services. This paper proposes a novel deep learning model based on the gated recurrent neural network and convolution neural network to understand human emotion from speech to improve their IVA services and monitor their mental health.