EME-TTS：解锁语音合成中的重音与情感联系
EME-TTS: Unlocking the Emphasis and Emotion Link in Speech Synthesis
AI总结：
本文提出EME-TTS框架，通过弱监督学习和重音感知增强模块，结合大语言模型预测重音位置，解决了情感语音合成中重音与情感交互不足的问题，实现了更自然的情感表达与稳定的目标重音控制。
摘要：
近年来，情感文本转语音（TTS）合成与重音可控语音合成取得了显著进展。然而，两者之间的交互作用仍未被充分探索。我们提出了Emphasis Meets Emotion TTS（EME-TTS），这是一个旨在解决两个关键研究问题的新型框架：（1）如何有效利用重音来增强情感语音的表现力，以及（2）如何在不同情感下保持目标重音的感知清晰度与稳定性。EME-TTS采用带有重音伪标签的弱监督学习和基于方差的重音特征。此外，提出的重音感知增强（EPE）模块增强了情感信号与重音位置之间的交互。实验结果表明，EME-TTS结合大语言模型进行重音位置预测时，能够实现更自然的情感语音合成，同时在不同情感下保持稳定且可区分的目标重音。合成样本可在线获取。
英文摘要：
In recent years, emotional Text-to-Speech (TTS) synthesis and emphasis-controllable speech synthesis have advanced significantly. However, their interaction remains underexplored. We propose Emphasis Meets Emotion TTS (EME-TTS), a novel framework designed to address two key research questions: (1) how to effectively utilize emphasis to enhance the expressiveness of emotional speech, and (2) how to maintain the perceptual clarity and stability of target emphasis across different emotions. EME-TTS employs weakly supervised learning with emphasis pseudo-labels and variance-based emphasis features. Additionally, the proposed Emphasis Perception Enhancement (EPE) block enhances the interaction between emotional signals and emphasis positions. Experimental results show that EME-TTS, when combined with large language models for emphasis position prediction, enables more natural emotional speech synthesis while preserving stable and distinguishable target emphasis across emotions. Synthesized samples are available on-line.
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