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Learning Hierarchical Representations for Expressive Speaking Style in End-to-End Speech Synthesis

2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)(2019)

Shaanxi Provincial Key Laboratory of Speech and Image Information Processing | ByteDance AI Lab

Cited 22|Views7
Abstract
Although Global Style Tokens (GSTs) are a recently-proposed method to uncover expressive factors of variation in speaking style, they are a mixture of style attributes without explicitly considering the factorization of multiple-level speaking styles. In this work, we introduce a hierarchical GST architecture with residuals to Tacotron, which learns multiple-level disentangled representations to model and control different style granularities in synthesized speech. We make hierarchical evaluations conditioned on individual tokens from different GST layers. As the number of layers increases, we tend to observe a coarse to fine style decomposition. For example, the first GST layer learns a good representation of speaker IDs while finer speaking style or emotion variations can be found in higher-level layers. Meanwhile, the proposed model shows good performance of style transfer.
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Speaking style,disentangled representations,hierarchical GST,style transfer
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要点】:本文提出了一种分层全局风格令牌(GST)架构,通过残差与 Tacotron 结合,学习多级分解的表示,以模型化和控制合成语音中不同风格的粒度,其创新点在于能够对 speaking style 进行多级别分解并控制。

方法】:研究采用了一种分层的 GST 架构,并将其与 Tacotron 结合。

实验】:通过在不同的 GST 层上对单个令牌进行层次化评估,实验显示随着层数的增加,风格分解从粗到细。例如,第一层 GST 层能够很好地表示说话者 ID,而更高层的则可以发现更细致的说话风格或情感变化。同时,所提出的模型在风格转换方面表现良好。