# DeepEmoNet: Building Machine Learning Models for Automatic Emotion Recognition in Human Speeches

arXiv: https://arxiv.org/abs/2509.00025 · HTML: https://mlsanity.com/paper/2509.00025/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 22 references that are in the map.
- First posted on arXiv 20 Aug 2025.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [fastai: A Layered API for Deep Learning](https://mlsanity.com/paper/2002.04688/) (2020)
- [End-to-End Multimodal Emotion Recognition using Deep Neural Networks](https://mlsanity.com/paper/1704.08619/) (2017)

## Abstract (arXiv)

Speech emotion recognition (SER) has been a challenging problem in spoken language processing research, because it is unclear how human emotions are connected to various components of sounds such as pitch, loudness, and energy. This paper aims to tackle this problem using machine learning. Particularly, we built several machine learning models using SVMs, LTSMs, and CNNs to classify emotions in human speeches. In addition, by leveraging transfer learning and data augmentation, we efficiently trained our models to attain decent performances on a relatively small dataset. Our best model was a ResNet34 network, which achieved an accuracy of 66.7% and an F1 score of 0.631.