# EmoAugNet: A Signal-Augmented Hybrid CNN-LSTM Framework for Speech Emotion Recognition

arXiv: https://arxiv.org/abs/2508.06321 · HTML: https://mlsanity.com/paper/2508.06321/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 25 references that are in the map.
- First posted on arXiv 6 Aug 2025.

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

- [Deep Speech 2: End-to-End Speech Recognition in English and Mandarin](https://mlsanity.com/paper/1512.02595/) (2015)
- [CNN+LSTM Architecture for Speech Emotion Recognition with Data Augmentation](https://mlsanity.com/paper/1802.05630/) (2018)
- [DST: Deformable Speech Transformer for Emotion Recognition](https://mlsanity.com/paper/2302.13729/) (2023)
- [An Ensemble 1D-CNN-LSTM-GRU Model with Data Augmentation for Speech Emotion Recognition](https://mlsanity.com/paper/2112.05666/) (2021)
- [The IBM 2015 English Conversational Telephone Speech Recognition System](https://mlsanity.com/paper/1505.05899/) (2015)
- [Letter-Based Speech Recognition with Gated ConvNets](https://mlsanity.com/paper/1712.09444/) (2017)

## Abstract (arXiv)

Recognizing emotional signals in speech has a significant impact on enhancing the effectiveness of human-computer interaction (HCI). This study introduces EmoAugNet, a hybrid deep learning framework, that incorporates Long Short-Term Memory (LSTM) layers with one-dimensional Convolutional Neural Networks (1D-CNN) to enable reliable Speech Emotion Recognition (SER). The quality and variety of the features that are taken from speech signals have a significant impact on how well SER systems perform. A comprehensive speech data augmentation strategy was used to combine both traditional methods, such as noise addition, pitch shifting, and time stretching, with a novel combination-based augmentation pipeline to enhance generalization and reduce overfitting. Each audio sample was transformed into a high-dimensional feature vector using root mean square energy (RMSE), Mel-frequency Cepstral Coefficient (MFCC), and zero-crossing rate (ZCR). Our model with ReLU activation has a weighted accuracy of 95.78% and unweighted accuracy of 92.52% on the IEMOCAP dataset and, with ELU activation, has a weighted accuracy of 96.75% and unweighted accuracy of 91.28%. On the RAVDESS dataset, we get a weighted accuracy of 94.53% and 94.98% unweighted accuracy for ReLU activation and 93.72% weighted accuracy and 94.64% unweighted accuracy for ELU activation. These results highlight EmoAugNet's effectiveness in improving the robustness and performance of SER systems through integated data augmentation and hybrid modeling.