English(EN) EmoNet: A Lightweight Attention-Based CNN for Speech Emotion Recognition

轻量级CNN-LSTM模型EmoNet在语音情感识别中达到96.75%的准确率

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-28 04:00

研究人员开发了EmoNet，这是一种专为语音情感识别设计的轻量级深度学习模型。该模型结合了深度可分离CNN与Squeeze-and-Excitation注意力机制、双向LSTM以及多头注意力池化。EmoNet在TESS、RAVDESS和SAVEE的组合数据集上实现了96.75%的高准确率，同时保持了411万的低参数量，使其适用于资源受限的环境。 AI

影响这种轻量级模型可以实现实时应用和计算能力有限的设备上更高效的语音情感识别。

排序理由该集群包含一篇详细介绍语音情感识别新模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Muhammad Moiz Khalid, Abdur Rehman Tariq, Muhammad Okasha Yamin · 2026-10-28 04:00

EmoNet：一种轻量级基于注意力的CNN用于语音情感识别

arXiv:2610.33977v1 Announce Type: cross Abstract: 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 classif…

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EmoNet：一种轻量级基于注意力的CNN用于语音情感识别

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