用深度学习自动识别语音中的情绪，提升小数据下的识别准确率。
DeepEmoNet: Building Machine Learning Models for Automatic Emotion Recognition in Human Speeches
- 结合ResNet34与迁移学习、数据增强技术
- 在小数据集上达66.7%准确率和0.631 F1分数
- 适合语音情感分析、人机交互场景应用
语音情感识别（SER）是语音处理中的难点，因人类情绪与音高、响度、能量等声音特征的关联尚不明确。本文采用机器学习方法构建SVM、LSTM、CNN模型进行语音情绪分类。通过迁移学习与数据增强，在较小数据集上高效训练模型。最佳模型为ResNet34网络，在测试中取得66.7%的准确率和0.631的F1分数，验证了方法在小样本场景下的有效性。
原文摘要 · Abstract (English)
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 $0.631$.
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