  End-to-end neural TTS has achieved superior performance on reading style
speech synthesis. However, it's still a challenge to build a high-quality
conversational TTS due to the limitations of the corpus and modeling
capability. This study aims at building a conversational TTS for a voice agent
under sequence to sequence modeling framework. We firstly construct a
spontaneous conversational speech corpus well designed for the voice agent with
a new recording scheme ensuring both recording quality and conversational
speaking style. Secondly, we propose a conversation context-aware end-to-end
TTS approach which has an auxiliary encoder and a conversational context
encoder to reinforce the information about the current utterance and its
context in a conversation as well. Experimental results show that the proposed
methods produce more natural prosody in accordance with the conversational
context, with significant preference gains at both utterance-level and
conversation-level. Moreover, we find that the model has the ability to express
some spontaneous behaviors, like fillers and repeated words, which makes the
conversational speaking style more realistic.
