GraphSpeech: Syntax-Aware Graph Attention Network For Neural Speech Synthesis

• URL: http://arxiv.org/abs/2010.12423v3
• Date: Fri, 26 Mar 2021 13:21:02 GMT
• Title: GraphSpeech: Syntax-Aware Graph Attention Network For Neural Speech Synthesis
• Authors: Rui Liu, Berrak Sisman and Haizhou Li
• Abstract summary: Transformer-based end-to-end text-to-speech synthesis (TTS) is one of such successful implementations. We propose a novel neural TTS model, denoted as GraphSpeech, that is formulated under graph neural network framework. Experiments show that GraphSpeech consistently outperforms the Transformer TTS baseline in terms of spectrum and prosody rendering of utterances.
• Score: 79.1885389845874
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Attention-based end-to-end text-to-speech synthesis (TTS) is superior to conventional statistical methods in many ways. Transformer-based TTS is one of such successful implementations. While Transformer TTS models the speech frame sequence well with a self-attention mechanism, it does not associate input text with output utterances from a syntactic point of view at sentence level. We propose a novel neural TTS model, denoted as GraphSpeech, that is formulated under graph neural network framework. GraphSpeech encodes explicitly the syntactic relation of input lexical tokens in a sentence, and incorporates such information to derive syntactically motivated character embeddings for TTS attention mechanism. Experiments show that GraphSpeech consistently outperforms the Transformer TTS baseline in terms of spectrum and prosody rendering of utterances.

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