  Current end-to-end autoregressive TTS systems (e.g. Tacotron 2) have
outperformed traditional parallel approaches on the quality of synthesized
speech. However, they introduce new problems at the same time. Due to the
autoregressive nature, the time cost of inference has to be proportional to the
length of text, which pose a great challenge for online serving. On the other
hand, the style of synthetic speech becomes unstable and may change obviously
among sentences. In this paper, we propose a Phrase based Parallel End-to-End
TTS System (PPSpeech) to address these issues. PPSpeech uses autoregression
approach within a phrase and executes parallel strategies for different
phrases. By this method, we can achieve both high quality and high efficiency.
In addition, we propose acoustic embedding and text context embedding as the
conditions of encoder to keep successive and prevent from abrupt style or
timbre change. Experiments show that, the synthesis speed of PPSpeech is much
faster than sentence level autoregressive Tacotron 2 when a sentence has more
than 5 phrases. The speed advantage increases with the growth of sentence
length. Subjective experiments show that the proposed system with acoustic
embedding and context embedding as conditions can make the style transition
across sentences gradient and natural, defeating Global Style Token (GST)
obviously in MOS.
