Speech Emotion Recognition using Supervised Deep Recurrent System for Mental Health Monitoring
This addresses mental health monitoring for users of virtual assistants, but it appears incremental as it combines existing neural network architectures without claiming a breakthrough.
The paper tackles the problem of early detection of mental health issues by proposing a deep learning model for speech emotion recognition, aiming to improve Intelligent Virtual Personal Assistant services and monitor mental health, though no concrete results or numbers are provided in the 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.