基于半监督对比学习的多说话人表现力语音合成提升方法
Boosting Multi-Speaker Expressive Speech Synthesis with Semi-supervised Contrastive Learning
AI总结：
本文提出基于半监督对比学习的多说话人TTS方法，通过多层级对比学习提取解耦的风格、情感与说话人表征，并结合半监督策略利用多领域数据，整合至改进VITS模型，实现跨说话人的风格与情感迁移及表现力语音合成。
摘要：
本文旨在构建一个多说话人表现力TTS系统，为特定说话人合成包含多种风格与情感的语音。为此，我们提出一种基于对比学习的TTS方法，以实现跨说话人的风格与情感迁移。具体而言，利用句子级和类别级等不同层级的对比学习，从语音中提取解耦的风格、情感与说话人表征，用于风格和情感迁移。此外，引入半监督训练策略，通过结合风格标注数据、情感标注数据及大量无标签数据等多领域数据，提升数据利用效率。为实现特定说话人具有多样风格与情感的表现力语音，将学习到的解耦表征整合到改进的VITS模型中。在多领域数据上的实验证明了所提方法的有效性。
英文摘要：
This paper aims to build a multi-speaker expressive TTS system, synthesizing a target speaker's speech with multiple styles and emotions. To this end, we propose a novel contrastive learning-based TTS approach to transfer style and emotion across speakers. Specifically, contrastive learning from different levels, i.e. utterance and category level, is leveraged to extract the disentangled style, emotion, and speaker representations from speech for style and emotion transfer. Furthermore, a semi-supervised training strategy is introduced to improve the data utilization efficiency by involving multi-domain data, including style-labeled data, emotion-labeled data, and abundant unlabeled data. To achieve expressive speech with diverse styles and emotions for a target speaker, the learned disentangled representations are integrated into an improved VITS model. Experiments on multi-domain data demonstrate the effectiveness of the proposed method.
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。
