揭示说话人嵌入中的情感聚类：一种用于语音情感识别的对比学习策略
Revealing Emotional Clusters in Speaker Embeddings: A Contrastive Learning Strategy for Speech Emotion Recognition
AI总结：
本研究揭示了说话人嵌入中存在情感聚类现象，并提出一种基于聚类的对比预训练策略，利用情感未标记数据显著提升了语音情感识别性能。
摘要：
说话人嵌入携带了有价值的情感相关信息，这使其成为增强语音情感识别（SER）的有前景资源，尤其是在标注数据有限的情况下。传统上，人们假设情感信息间接嵌入在说话人嵌入中，导致其未被充分利用。我们的研究揭示了情感与最先进的说话人嵌入之间以说话人内部聚类形式存在的直接且有用的联系。通过进行详尽的聚类分析，我们证明情感信息可以很容易地从说话人嵌入中提取出来。为了利用这些信息，我们引入了一种新的对比预训练方法，应用于情感未标记数据以进行语音情感识别。所提出的方法涉及基于说话人嵌入的说话人内部聚类来采样正例和负例。所提出的策略利用了大量情感未标记数据，无论是作为独立的预训练任务还是集成到多任务预训练设置中，都显著提升了SER的性能。
英文摘要：
Speaker embeddings carry valuable emotion-related information, which makes them a promising resource for enhancing speech emotion recognition (SER), especially with limited labeled data. Traditionally, it has been assumed that emotion information is indirectly embedded within speaker embeddings, leading to their under-utilization. Our study reveals a direct and useful link between emotion and state-of-the-art speaker embeddings in the form of intra-speaker clusters. By conducting a thorough clustering analysis, we demonstrate that emotion information can be readily extracted from speaker embeddings. In order to leverage this information, we introduce a novel contrastive pretraining approach applied to emotion-unlabeled data for speech emotion recognition. The proposed approach involves the sampling of positive and the negative examples based on the intra-speaker clusters of speaker embeddings. The proposed strategy, which leverages extensive emotion-unlabeled data, leads to a significant improvement in SER performance, whether employed as a standalone pretraining task or integrated into a multi-task pretraining setting.
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。
