StyleSpeech：面向有声书表现力语音合成的基于VQ-VAE预训练的自监督风格增强方法
StyleSpeech: Self-supervised Style Enhancing with VQ-VAE-based Pre-training for Expressive Audiobook Speech Synthesis
AI总结：
针对有声书语音合成表现力受限及风格数据分布不均问题，提出基于VQ-VAE预训练的自监督风格增强方法，通过双路径架构分别建模发音与风格，有效提升合成语音的自然度与表现力。
摘要：
有声书合成语音的表现力受限于通用化模型架构以及训练数据中风格分布不均衡的问题。为解决这些问题，本文提出一种面向有声书表现力语音合成的自监督风格增强方法，该方法基于VQ-VAE（矢量量化变分自编码器）预训练。首先，利用大量无标注纯文本数据预训练文本风格编码器；其次，基于VQ-VAE的频谱风格提取器以自监督方式，借助覆盖复杂风格变化的大量音频数据完成预训练；随后专门设计了具备两条编码器-解码器路径的新型架构，分别在风格提取器的引导下建模发音与高层风格表现力。客观与主观评估均表明，所提方法能有效提升有声书合成中语音的自然度与表现力，尤其在角色场景与域外场景下效果显著。
英文摘要：
The expressive quality of synthesized speech for audiobooks is limited by generalized model architecture and unbalanced style distribution in the training data. To address these issues, in this paper, we propose a self-supervised style enhancing method with VQ-VAE-based pre-training for expressive audiobook speech synthesis. Firstly, a text style encoder is pre-trained with a large amount of unlabeled text-only data. Secondly, a spectrogram style extractor based on VQ-VAE is pre-trained in a self-supervised manner, with plenty of audio data that covers complex style variations. Then a novel architecture with two encoder-decoder paths is specially designed to model the pronunciation and high-level style expressiveness respectively, with the guidance of the style extractor. Both objective and subjective evaluations demonstrate that our proposed method can effectively improve the naturalness and expressiveness of the synthesized speech in audiobook synthesis especially for the role and out-of-domain scenarios.
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。
