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

Nelly Elsayed; Zag ElSayed; Navid Asadizanjani; Murat Ozer; Ahmed Abdelgawad; Magdy Bayoumi

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

Audio and Speech Processing 2022-10-28 v3 Human-Computer Interaction Machine Learning Sound

Authors: Nelly Elsayed , Zag ElSayed , Navid Asadizanjani , Murat Ozer , Ahmed Abdelgawad , Magdy Bayoumi

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.

Keywords

speech emotion recognition gated recurrent neural network convolutional neural network mental health monitoring intelligent virtual personal assistant

Cite

@article{arxiv.2208.12812,
  title  = {Speech Emotion Recognition using Supervised Deep Recurrent System for Mental Health Monitoring},
  author = {Nelly Elsayed and Zag ElSayed and Navid Asadizanjani and Murat Ozer and Ahmed Abdelgawad and Magdy Bayoumi},
  journal= {arXiv preprint arXiv:2208.12812},
  year   = {2022}
}

Comments

6 pages, 5 figures, 3 tables, accepted in the IEEE WFIoT2022

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

Abstract

Keywords

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