@inproceedings{528d5d5f6d4b4725b143b3b175136e8d,
title = "Speech Emotion Recognition using Supervised Deep Recurrent System for Mental Health Monitoring",
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. Nowa-days, 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 = "GRU, Speech emotion recognition, intelligent personal assistants, mental health, speech detection",
author = "Nelly Elsayed and Zag Elsayed and Navid Asadizanjani and Murat Ozer and Ahmed Abdelgawad and Magdy Bayoumi",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 8th IEEE World Forum on Internet of Things, WF-IoT 2022 ; Conference date: 26-10-2022 Through 11-11-2022",
year = "2022",
doi = "10.1109/WF-IoT54382.2022.10152117",
language = "English",
series = "2022 IEEE 8th World Forum on Internet of Things, WF-IoT 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2022 IEEE 8th World Forum on Internet of Things, WF-IoT 2022",
}
