基于监督对比学习的跨语料库语音情感识别方法
A Cross-Corpus Speech Emotion Recognition Method Based on Supervised Contrastive Learning
AI总结：
针对语音情感识别中大规模公开数据集缺乏、跨分布数据泛化能力有限的问题，提出基于监督对比学习的两阶段微调跨语料库方法，在IEMOCAP和CASIA数据集上取得优于现有最佳结果的无加权准确率。
摘要：
语音情感识别（SER）研究常面临缺乏大规模公开数据集、处理不同分布数据时泛化能力有限等挑战。为解决该问题，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流程：首先，在多个语音情感数据集上通过监督对比学习对自监督语音表征模型进行微调；然后，在目标数据集上对分类器进行微调。实验结果显示，基于WavLM的模型在IEMOCAP数据集上取得77.41%的无加权准确率（UA），在CASIA数据集上取得96.49%的UA，优于这两个数据集上的现有最佳结果。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
