ContextSpeech: Expressive and Efficient Text-to-Speech for Paragraph Reading

Although Text-to-Speech (TTS) has made rapid progress in speech quality at sentence level, it still faces a lot of challenges in paragraph / long-form reading. Synthesizing sentence by sentence in a paragraph and then concatenating them together will cause inconsistent issues that affect paragraph-level expressiveness. While directly modelling all the sentences in a paragraph will incur large computation / memory cost. In this paper, we develop a TTS system called ContextSpeech, which models the contextual information in a paragraph for coherence and expressiveness without largely increasing the computation or memory cost. On the one hand, we introduce a memory-cached recurrence mechanism to let the current sentence see more history information both on the text and speech sides. On the other hand, we construct text-based semantic information in a hierarchical structure, which can broaden the horizon and incorporate the future information. Additionally, we use a linearized self-attention with compatible relative-position encoding to reduce the computation / memory cost. Experiments show that ContextSpeech significantly improves the paragraph-level voice quality and prosody expressiveness in terms of both subjective and objective evaluation metrics. Furthermore, ContextSpeech achieves better model efficiency in both training and inference stage.