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Naihan Li

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AAAI Conference 2020 Conference Paper

RobuTrans: A Robust Transformer-Based Text-to-Speech Model

Naihan Li
Yanqing Liu
Yu Wu
Shujie Liu
Sheng Zhao
Ming Liu

Recently, neural network based speech synthesis has achieved outstanding results, by which the synthesized audios are of excellent quality and naturalness. However, current neural TTS models suffer from the robustness issue, which results in abnormal audios (bad cases) especially for unusual text (unseen context). To build a neural model which can synthesize both natural and stable audios, in this paper, we make a deep analysis of why the previous neural TTS models are not robust, based on which we propose RobuTrans (Robust Transformer), a robust neural TTS model based on Transformer. Comparing to TransformerTTS, our model ﬁrst converts input texts to linguistic features, including phonemic features and prosodic features, then feed them to the encoder. In the decoder, the encoder-decoder attention is replaced with a duration-based hard attention mechanism, and the causal self-attention is replaced with a ”pseudo non-causal attention” mechanism to model the holistic information of the input. Besides, the position embedding is replaced with a 1- D CNN, since it constrains the maximum length of synthesized audio. With these modiﬁcations, our model not only ﬁx the robustness problem, but also achieves on parity MOS (4. 36) with TransformerTTS (4. 37) and Tacotron2 (4. 37) on our general set.

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AAAI Conference 2019 Conference Paper

Neural Speech Synthesis with Transformer Network

Naihan Li
Shujie Liu
Yanqing Liu
Sheng Zhao
Ming Liu

Although end-to-end neural text-to-speech (TTS) methods (such as Tacotron2) are proposed and achieve state-of-theart 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 training efficiency. Meanwhile, any two inputs at different times are connected directly by a 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).

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