EmoNet：轻量级注意力 CNN 实现语音情感识别，仅 411 万参数
EmoNet: A Lightweight Attention-Based CNN for Speech Emotion Recognition
一篇轻量语音情感识别论文：不用大 Transformer，411 万参数就在三个数据集组合上跑出 96.75% 准确率，做端侧部署的可以看看。
研究团队提出 EmoNet，一个面向语音情感识别（SER）的轻量级深度学习框架，可对七类情感进行分类。模型采用四块深度可分离 CNN 编码器，结合 squeeze-and-excitation 注意力、残差连接、双向 LSTM 与多头注意力池化。输入端使用 134 通道声学特征，并配合 SpecAugment 等数据增强。在 TESS、RAVDESS、SAVEE 组合数据集上，EmoNet 测试准确率达 96.75%，参数量仅 411 万，可与规模大得多的 Transformer 架构竞争。
EmoNet: A Lightweight Attention-Based CNN for Speech Emotion Recognition
Speech Emotion Recognition (SER) is a fundamental task in affective computing that enables applications such as mental health assessment, human-computer interaction, and automated customer support systems. Accurate emotion classification from speech remains challenging due to variations in speaker style, acoustic conditions, and the high computational cost of modern deep learning approaches. Many existing systems depend on large transformer-based architectures that require substantial computational resources, making them less suitable for real-time or resource-constrained environments. This research proposes EmoNet, a lightweight deep learning framework for speech emotion classification across seven emotion categories. The model is built on a four-block depthwise separable CNN encoder with squeeze-and-excitation attention and residual connections, combined with a Bidirectional LSTM for temporal feature learning and a multi-head attention pooling mechanism for improved representation. A comprehensive 134-channel acoustic feature set alongside data augmentation techniques including audio-level perturbations and SpecAugment is employed to improve robustness under diverse acoustic conditions. Evaluated on a combined dataset of TESS, RAVDESS, and SAVEE, EmoNet achieves a test accuracy of 96.75% with only 4.11 million parameters. The proposed model outperforms its reproduced baseline and remains competitive with significantly larger state-of-the-art architectures.