# Toward Efficient Speech Emotion Recognition via Spectral Learning and Attention

arXiv: https://arxiv.org/abs/2507.03251 · HTML: https://mlsanity.com/paper/2507.03251/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 67 references that are in the map.
- First posted on arXiv 4 Jul 2025.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations](https://mlsanity.com/paper/1810.02508/) (2018)
- [Gate-Variants of Gated Recurrent Unit (GRU) Neural Networks](https://mlsanity.com/paper/1701.05923/) (2017)
- [Cross-Entropy Loss Functions: Theoretical Analysis and Applications](https://mlsanity.com/paper/2304.07288/) (2023)
- [Dawn of the transformer era in speech emotion recognition: closing the valence gap](https://mlsanity.com/paper/2203.07378/) (2022)
- [GCNet: Graph Completion Network for Incomplete Multimodal Learning in Conversation](https://mlsanity.com/paper/2203.02177/) (2022)
- [COLD Fusion: Calibrated and Ordinal Latent Distribution Fusion for Uncertainty-Aware Multimodal Emotion Recognition](https://mlsanity.com/paper/2206.05833/) (2022)
- [Deep Learning for Visual Speech Analysis: A Survey](https://mlsanity.com/paper/2205.10839/) (2022)
- [Self-paced ensemble learning for speech and audio classification](https://mlsanity.com/paper/2103.11988/) (2021)
- [Describing emotions with acoustic property prompts for speech emotion recognition](https://mlsanity.com/paper/2211.07737/) (2022)
- [Novel Dual-Channel Long Short-Term Memory Compressed Capsule Networks for Emotion Recognition](https://mlsanity.com/paper/2112.13350/) (2021)
- [Evaluating raw waveforms with deep learning frameworks for speech emotion recognition](https://mlsanity.com/paper/2307.02820/) (2023)

## Abstract (arXiv)

Speech Emotion Recognition (SER) traditionally relies on auditory data analysis for emotion classification. Several studies have adopted different methods for SER. However, existing SER methods often struggle to capture subtle emotional variations and generalize across diverse datasets. In this article, we use Mel-Frequency Cepstral Coefficients (MFCCs) as spectral features to bridge the gap between computational emotion processing and human auditory perception. To further improve robustness and feature diversity, we propose a novel 1D-CNN-based SER framework that integrates data augmentation techniques. MFCC features extracted from the augmented data are processed using a 1D Convolutional Neural Network (CNN) architecture enhanced with channel and spatial attention mechanisms. These attention modules allow the model to highlight key emotional patterns, enhancing its ability to capture subtle variations in speech signals. The proposed method delivers cutting-edge performance, achieving the accuracy of 97.49% for SAVEE, 99.23% for RAVDESS, 89.31% for CREMA-D, 99.82% for TESS, 99.53% for EMO-DB, and 96.39% for EMOVO. Experimental results show new benchmarks in SER, demonstrating the effectiveness of our approach in recognizing emotional expressions with high precision. Our evaluation demonstrates that the integration of advanced Deep Learning (DL) methods substantially enhances generalization across diverse datasets, underscoring their potential to advance SER for real-world deployment in assistive technologies and human-computer interaction.