While state-of-the-art Text-to-Speech systems can generate natural speech of
very high quality at sentence level, they still meet great challenges in speech
generation for paragraph / long-form reading. Such deficiencies are due to i)
ignorance of cross-sentence contextual information, and ii) high computation
and memory cost for long-form synthesis. To address these issues, this work
develops a lightweight yet effective TTS system, ContextSpeech. Specifically,
we first design a memory-cached recurrence mechanism to incorporate global text
and speech context into sentence encoding. Then we construct
hierarchically-structured textual semantics to broaden the scope for global
context enhancement. Additionally, we integrate linearized self-attention to
improve model efficiency. Experiments show that ContextSpeech significantly
improves the voice quality and prosody expressiveness in paragraph reading with
competitive model efficiency. Audio samples are available at:
this https URL

该论文提出了一种轻量级而有效的 TTS 系统 ContextSpeech，采用记忆缓存的复现机制和分层结构的文本语义，以及线性化的自注意力机制来增强全局文本和语音语境，并在长篇阅读中显着提高了语音质量和语调表现力。