  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:
https://contextspeech.github.io/demo/
