Neural Speech Synthesis with Transformer Network

TL;DR

This paper introduces a Transformer-based neural TTS model that significantly improves training efficiency and long-range dependency modeling, achieving state-of-the-art speech synthesis quality close to human levels.

Contribution

The paper adapts Transformer multi-head self-attention to neural TTS, replacing RNNs, resulting in faster training and better long-term dependency modeling.

Findings

Training speed increased by 4.25 times compared to Tacotron2

Achieved state-of-the-art performance with a MOS gap of 0.048

Generated speech quality close to human level

Abstract

Although end-to-end neural text-to-speech (TTS) methods (such as Tacotron2) are proposed and achieve state-of-the-art performance, they still suffer from two problems: 1) low efficiency during training and inference; 2) hard to model long dependency using current recurrent neural networks (RNNs). Inspired by the success of Transformer network in neural machine translation (NMT), in this paper, we introduce and adapt the multi-head attention mechanism to replace the RNN structures and also the original attention mechanism in Tacotron2. With the help of multi-head self-attention, the hidden states in the encoder and decoder are constructed in parallel, which improves the training efficiency. Meanwhile, any two inputs at different times are connected directly by self-attention mechanism, which solves the long range dependency problem effectively. Using phoneme sequences as input, our Transformer TTS network generates mel spectrograms, followed by a WaveNet vocoder to output the final audio results. Experiments are conducted to test the efficiency and performance of our new network. For the efficiency, our Transformer TTS network can speed up the training about 4.25 times faster compared with Tacotron2. For the performance, rigorous human tests show that our proposed model achieves state-of-the-art performance (outperforms Tacotron2 with a gap of 0.048) and is very close to human quality (4.39 vs 4.44 in MOS).