  Transformer-based text to speech (TTS) model (e.g., Transformer
TTS~\cite{li2019neural}, FastSpeech~\cite{ren2019fastspeech}) has shown the
advantages of training and inference efficiency over RNN-based model (e.g.,
Tacotron~\cite{shen2018natural}) due to its parallel computation in training
and/or inference. However, the parallel computation increases the difficulty
while learning the alignment between text and speech in Transformer, which is
further magnified in the multi-speaker scenario with noisy data and diverse
speakers, and hinders the applicability of Transformer for multi-speaker TTS.
In this paper, we develop a robust and high-quality multi-speaker Transformer
TTS system called MultiSpeech, with several specially designed
components/techniques to improve text-to-speech alignment: 1) a diagonal
constraint on the weight matrix of encoder-decoder attention in both training
and inference; 2) layer normalization on phoneme embedding in encoder to better
preserve position information; 3) a bottleneck in decoder pre-net to prevent
copy between consecutive speech frames. Experiments on VCTK and LibriTTS
multi-speaker datasets demonstrate the effectiveness of MultiSpeech: 1) it
synthesizes more robust and better quality multi-speaker voice than naive
Transformer based TTS; 2) with a MutiSpeech model as the teacher, we obtain a
strong multi-speaker FastSpeech model with almost zero quality degradation
while enjoying extremely fast inference speed.
