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

arXiv: https://arxiv.org/abs/2208.12812 · HTML: https://mlsanity.com/paper/2208.12812/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 61 references that are in the map.
- First posted on arXiv 26 Aug 2022.

## Builds on (most cited first, 6 of 6)

- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [1D Convolutional Neural Networks and Applications: A Survey](https://mlsanity.com/paper/1905.03554/) (2019)
- [Dilated Convolution with Dilated GRU for Music Source Separation](https://mlsanity.com/paper/1906.01203/) (2019)
- [Improving Automatic Emotion Recognition from speech using Rhythm and Temporal feature](https://mlsanity.com/paper/1303.1761/) (2013)
- [Biologically inspired speech emotion recognition](https://mlsanity.com/paper/2111.08112/) (2021)

## Built on by (most cited first, 15 of 15)

- [Synthetic Data Generation by Supervised Neural Gas Network for Physiological Emotion Recognition Data](https://mlsanity.com/paper/2501.16353/) (2025)
- [EMO-Debias: Benchmarking Gender Debiasing Techniques in Multi-Label Speech Emotion Recognition](https://mlsanity.com/paper/2506.04652/) (2025)
- [Active Learning Based Fine-Tuning Framework for Speech Emotion Recognition](https://mlsanity.com/paper/2310.00283/) (2023)
- [Multi-Teacher Language-Aware Knowledge Distillation for Multilingual Speech Emotion Recognition](https://mlsanity.com/paper/2506.08717/) (2025)
- [LiteLSTM Architecture Based on Weights Sharing for Recurrent Neural Networks](https://mlsanity.com/paper/2301.04794/) (2023)
- [CO-VADA: A Confidence-Oriented Voice Augmentation Debiasing Approach for Fair Speech Emotion Recognition](https://mlsanity.com/paper/2506.06071/) (2025)
- [Human Behavior in the Time of COVID-19: Learning from Big Data](https://mlsanity.com/paper/2303.13452/) (2023)
- [TemporalAugmenter: An Ensemble Recurrent Based Deep Learning Approach for Signal Classification](https://mlsanity.com/paper/2401.06970/) (2024)
- [Active Learning with Task Adaptation Pre-training for Speech Emotion Recognition](https://mlsanity.com/paper/2405.00307/) (2024)
- [WavFusion: Towards wav2vec 2.0 Multimodal Speech Emotion Recognition](https://mlsanity.com/paper/2412.05558/) (2024)
- [Joint Learning using Mixture-of-Expert-Based Representation for Speech Enhancement and Robust Emotion Recognition](https://mlsanity.com/paper/2509.08470/) (2025)
- [MI-Fuse: Label Fusion for Unsupervised Domain Adaptation with Closed-Source Large-Audio Language Model](https://mlsanity.com/paper/2509.20706/) (2025)
- [Explainable Speech Emotion Recognition: Weighted Attribute Fairness to Model Demographic Contributions to Social Bias](https://mlsanity.com/paper/2604.19763/) (2026)
- [Explainable Lightweight Compact Deep Models for Speech Emotion Recognition](https://mlsanity.com/paper/2607.16803/) (2026)
- [Reading Emotions in the Token Space: Discriminative Adaptation of SpeechLLMs for Emotion Recognition](https://mlsanity.com/paper/2609.20081/) (2026)

## Abstract (arXiv)

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.