用对比学习提升跨语料库语音情感识别性能。
A Cross-Corpus Speech Emotion Recognition Method Based on Supervised Contrastive Learning
- 分两阶段微调：先用对比学习融合多数据集，再针对目标数据微调分类器。
- 基于WavLM模型，在IEMOCAP和CASIA上分别达到77.41%和96.49%的无加权准确率。
- 适合需要跨数据集泛化的语音情感识别研究者使用。
语音情感识别（SER）常面临大规模公开数据集缺失及跨分布数据泛化能力不足的问题。为此，本文提出一种基于监督对比学习的跨语料库语音情感识别方法。该方法采用两阶段微调：首先在多个语音情感数据集上，利用监督对比学习对自监督语音表示模型进行微调；然后在目标数据集上微调分类器。实验结果表明，基于WavLM的模型在IEMOCAP数据集上达到77.41%的无加权准确率（UA），在CASIA数据集上达到96.49%，优于当前最优结果。
原文摘要 · Abstract (English)
Research on Speech Emotion Recognition (SER) often faces challenges such as the lack of large-scale public datasets and limited generalization capability when dealing with data from different distributions. To solve this problem, this paper proposes a cross-corpus speech emotion recognition method based on supervised contrast learning. The method employs a two-stage fine-tuning process: first, the self-supervised speech representation model is fine-tuned using supervised contrastive learning on multiple speech emotion datasets; then, the classifier is fine-tuned on the target dataset. The experimental results show that the WavLM-based model achieved unweighted accuracy (UA) of 77.41% on the IEMOCAP dataset and 96.49% on the CASIA dataset, outperforming the state-of-the-art results on the two datasets.
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