Spotlight-TTS：通过浊音感知风格提取与风格方向调整突显风格以实现富有表现力的文本到语音合成
Spotlight-TTS: Spotlighting the Style via Voiced-Aware Style Extraction and Style Direction Adjustment for Expressive Text-to-Speech
AI总结：
提出Spotlight-TTS，通过浊音感知风格提取聚焦与风格高度相关的浊音区域并保持连续性，结合风格方向调整优化与TTS模型的整合，显著提升了合成语音的表现力、整体质量和风格迁移能力。
摘要：
富有表现力的文本到语音（TTS）的最新进展引入了多种基于从参考语音中提取的风格嵌入的方法。然而，合成高质量的表现力语音仍然具有挑战性。我们提出了Spotlight-TTS，它通过浊音感知风格提取和风格方向调整来专门强调风格。浊音感知风格提取聚焦于与风格高度相关的浊音区域，同时保持不同语音区域之间的连续性，以提升表现力。我们调整提取风格的实现方向，以实现与TTS模型的最优整合，从而提升语音质量。实验结果表明，在表现力、整体语音质量和风格迁移能力方面，与基线模型相比，Spotlight-TTS取得了更优的性能。我们的音频样本已公开提供。
英文摘要：
Recent advances in expressive text-to-speech (TTS) have introduced diverse methods based on style embedding extracted from reference speech. However, synthesizing high-quality expressive speech remains challenging. We propose Spotlight-TTS, which exclusively emphasizes style via voiced-aware style extraction and style direction adjustment. Voiced-aware style extraction focuses on voiced regions highly related to style while maintaining continuity across different speech regions to improve expressiveness. We adjust the direction of the extracted style for optimal integration into the TTS model, which improves speech quality. Experimental results demonstrate that Spotlight-TTS achieves superior performance compared to baseline models in terms of expressiveness, overall speech quality, and style transfer capability. Our audio samples are publicly available.
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。
