Emo-DNA：面向跨语料库语音情感识别的情感解耦与对齐学习
Emo-DNA: Emotion Decoupling and Alignment Learning for Cross-Corpus Speech Emotion Recognition
AI总结：
针对跨语料库语音情感识别中特征混合与缺乏区分性的问题，提出EMO-DNA框架，通过对比情感解耦与双层情感对齐学习情感相关的语料库不变特征，在多个跨语料库场景下取得优于现有最先进方法的性能。
摘要：
跨语料库语音情感识别（SER）旨在将从一个标注良好的语料库中推断语音情感的能力泛化到一个未标注的语料库中，由于两个语料库之间存在显著差异，这是一项极具挑战性的任务。现有方法通常基于无监督域适应（UDA），试图通过全局分布对齐来学习语料库不变特征，但遗憾的是，所得到的特征会与语料库特定特征混合，或者缺乏类别区分性。为了应对这些挑战，我们提出了一种用于跨语料库SER的新颖情感解耦与对齐学习框架（EMO-DNA），这是一种用于学习情感相关语料库不变特征的新颖UDA方法。EMO-DNA的创新之处有两点：对比情感解耦和双层情感对齐。一方面，我们的对比情感解耦通过对比解耦损失实现解耦学习，以增强情感相关特征与语料库特定特征之间的可分离性。另一方面，我们的双层情感对齐引入了自适应阈值伪标签技术，以选择高置信度目标样本进行类别级对齐，并执行语料库级对齐，从而共同引导模型跨语料库学习具有类别区分性的语料库不变特征。大量实验结果证明了EMO-DNA在多个跨语料库场景下优于现有最先进方法的卓越性能。源代码可在https://github.com/Jiaxin-Ye/Emo-DNA获取。
英文摘要：
Cross-corpus speech emotion recognition (SER) seeks to generalize the ability of inferring speech emotion from a well-labeled corpus to an unlabeled one, which is a rather challenging task due to the significant discrepancy between two corpora. Existing methods, typically based on unsupervised domain adaptation (UDA), struggle to learn corpus-invariant features by global distribution alignment, but unfortunately, the resulting features are mixed with corpus-specific features or not class-discriminative. To tackle these challenges, we propose a novel Emotion Decoupling aNd Alignment learning framework (EMO-DNA) for cross-corpus SER, a novel UDA method to learn emotion-relevant corpus-invariant features. The novelties of EMO-DNA are two-fold: contrastive emotion decoupling and dual-level emotion alignment. On one hand, our contrastive emotion decoupling achieves decoupling learning via a contrastive decoupling loss to strengthen the separability of emotion-relevant features from corpus-specific ones. On the other hand, our dual-level emotion alignment introduces an adaptive threshold pseudo-labeling to select confident target samples for class-level alignment, and performs corpus-level alignment to jointly guide model for learning class-discriminative corpus-invariant features across corpora. Extensive experimental results demonstrate the superior performance of EMO-DNA over the state-of-the-art methods in several cross-corpus scenarios. Source code is available at https://github.com/Jiaxin-Ye/Emo-DNA.
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。
