Abstract
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.
Abstract (translated)
近期，基于从参考语音中提取的风格嵌入（style embedding）的表达性文本到语音（TTS）方法得到了多样化的发展。然而，合成高质量且富有表现力的语音仍然具有挑战性。为此，我们提出了Spotlight-TTS系统，该系统通过声带活跃度感知的风格提取和风格方向调整来专门强调风格。 声带活跃度感知的风格提取专注于与风格高度相关的发声区域，并保持在不同语音区域间的连贯性以提升表现力。此外，我们还对提取出的风格的方向进行调整，以便其能够最佳地整合到TTS模型中，从而改善语音质量。 实验结果表明，在表达能力、整体语音质量和风格迁移能力方面，Spotlight-TTS相比基线模型取得了显著更优的表现。我们的音频样本可公开获取。
URL
https://arxiv.org/abs/2505.20868
