# FSER: Deep Convolutional Neural Networks for Speech Emotion Recognition

arXiv: https://arxiv.org/abs/2109.07916 · HTML: https://mlsanity.com/paper/2109.07916/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 21 references that are in the map.
- First posted on arXiv 15 Sep 2021.

## Builds on (most cited first, 4 of 4)

- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [The Effectiveness of Data Augmentation in Image Classification using Deep Learning](https://mlsanity.com/paper/1712.04621/) (2017)
- [Understanding data augmentation for classification: when to warp?](https://mlsanity.com/paper/1609.08764/) (2016)
- [Max-Pooling Dropout for Regularization of Convolutional Neural Networks](https://mlsanity.com/paper/1512.01400/) (2015)

## Built on by (most cited first, 1 of 1)

- [User Experience Estimation in Human-Robot Interaction Via Multi-Instance Learning of Multimodal Social Signals](https://mlsanity.com/paper/2507.23544/) (2025)

## Abstract (arXiv)

Using mel-spectrograms over conventional MFCCs features, we assess the abilities of convolutional neural networks to accurately recognize and classify emotions from speech data. We introduce FSER, a speech emotion recognition model trained on four valid speech databases, achieving a high-classification accuracy of 95,05%, over 8 different emotion classes: anger, anxiety, calm, disgust, happiness, neutral, sadness, surprise. On each benchmark dataset, FSER outperforms the best models introduced so far, achieving a state-of-the-art performance. We show that FSER stays reliable, independently of the language, sex identity, and any other external factor. Additionally, we describe how FSER could potentially be used to improve mental and emotional health care and how our analysis and findings serve as guidelines and benchmarks for further works in the same direction.