情感感知量化用于离散语音表示：对情感保留的分析
Emotion-Aware Quantization for Discrete Speech Representations: An Analysis of Emotion Preservation
AI总结：
研究探讨了残差向量量化对离散语音表示中情感信息的影响，提出情感感知量化方法以提升情感感知的保留度，并改进低比特率下的情感识别性能。
摘要：
现代语音系统越来越多地使用离散自监督语音表示用于压缩和与基于标记的模型集成，但其对情感信息的影响仍不明确。我们研究了残差向量量化（RVQ）如何从表示和任务层面重塑离散语音表示中的情感信息。我们的分析表明，激进的压缩会不成比例地损害情感，不同情感类别和模型架构的损失不均。为此，我们引入了情感感知量化，使用情感特定和情感偏向的代码书，以提升硬情感和软情感感知的保留度。我们进一步提出了Emo-Q，一种轻量级的路由量化方法，选择情感专用的代码书，从而在较低比特率下提高情感识别性能。这些结果突显了情感感知离散化在鲁棒情绪语音处理中的重要性。
英文摘要：
Modern speech systems increasingly use discretized self-supervised speech representations for compression and integration with token-based models, yet their impact on emotional information remains unclear. We study how residual vector quantization (RVQ) reshapes emotional information in discrete speech representations from both representation- and task-level perspectives. Our analysis shows that aggressive compression disproportionately degrades emotion, with uneven loss across emotion classes and model architectures. To address this, we introduce emotion-aware quantization using emotion-specific and emotion-biased codebooks, improving the preservation of both hard and soft emotion perception. We further propose Emo-Q, a lightweight routed quantization method that selects emotion-specialized codebooks, improving emotion recognition performance at lower bitrates. These results highlight the importance of emotion-aware discretization for robust affective speech processing.
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