This paper proposes a new end-to-end text-to-speech (E2E-TTS) model based on
neural machine translation (NMT). The proposed model consists of two
components; a non-autoregressive vector quantized variational autoencoder
(VQ-VAE) model and an autoregressive Transformer-NMT model. The VQ-VAE model
learns a mapping function from a speech waveform into a sequence of discrete
symbols, and then the Transformer-NMT model is trained to estimate this
discrete symbol sequence from a given input text. Since the VQ-VAE model can
learn such a mapping in a fully-data-driven manner, we do not need to consider
hyperparameters of the feature extraction required in the conventional E2E-TTS
models. Thanks to the use of discrete symbols, we can use various techniques
developed in NMT and automatic speech recognition (ASR) such as beam search,
subword units, and fusions with a language model. Furthermore, we can avoid an
over smoothing problem of predicted features, which is one of the common issues
in TTS. The experimental evaluation with the JSUT corpus shows that the
proposed method outperforms the conventional Transformer-TTS model with a
non-autoregressive neural vocoder in naturalness, achieving the performance
comparable to the reconstruction of the VQ-VAE model.

本文提出了一种基于神经机器翻译 (NMT) 的全自动文本语音 (E2E-TTS) 模型，该模型由非自回归向量量化变分自动编码器 (VQ-VAE) 模型和自回归 Transformer-NMT 模型两部分组成，实验结果表明，该模型在自然度方面表现优异，可与 VQ-VAE 模型的重构相媲美。