  End-to-end Text-to-speech (TTS) system can greatly improve the quality of
synthesised speech. But it usually suffers form high time latency due to its
auto-regressive structure. And the synthesised speech may also suffer from some
error modes, e.g. repeated words, mispronunciations, and skipped words. In this
paper, we propose a novel non-autoregressive, fully parallel end-to-end TTS
system (FPETS). It utilizes a new alignment model and the recently proposed
U-shape convolutional structure, UFANS. Different from RNN, UFANS can capture
long term information in a fully parallel manner. Trainable position encoding
and two-step training strategy are used for learning better alignments.
Experimental results show FPETS utilizes the power of parallel computation and
reaches a significant speed up of inference compared with state-of-the-art
end-to-end TTS systems. More specifically, FPETS is 600X faster than Tacotron2,
50X faster than DCTTS and 10X faster than Deep Voice3. And FPETS can generates
audios with equal or better quality and fewer errors comparing with other
system. As far as we know, FPETS is the first end-to-end TTS system which is
fully parallel.
