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Found 5 papers, 1 papers with code
Enhancing Speaking Styles in Conversational Text-to-Speech Synthesis with Graph-based Multi-modal Context Modeling
However, state-of-the-art context modeling methods in conversational TTS only model the textual information in context with a recurrent neural network (RNN).
Enhancing Word-Level Semantic Representation via Dependency Structure for Expressive Text-to-Speech Synthesis
Exploiting rich linguistic information in raw text is crucial for expressive text-to-speech (TTS).
Towards Multi-Scale Style Control for Expressive Speech Synthesis
This paper introduces a multi-scale speech style modeling method for end-to-end expressive speech synthesis.
Adversarially learning disentangled speech representations for robust multi-factor voice conversion
To increase the robustness of highly controllable style transfer on multiple factors in VC, we propose a disentangled speech representation learning framework based on adversarial learning.
Syntactic representation learning for neural network based TTS with syntactic parse tree traversal
Meanwhile, nuclear-norm maximization loss is introduced to enhance the discriminability and diversity of the embeddings of constituent labels.
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