End-to-end, autoregressive model-based TTS has shown significant performance
improvements over the conventional one. However, the autoregressive module
training is affected by the exposure bias, or the mismatch between the
different distributions of real and predicted data. While real data is
available in training, but in testing, only predicted data is available to feed
the autoregressive module. By introducing both real and generated data
sequences in training, we can alleviate the effects of the exposure bias. We
propose to use Generative Adversarial Network (GAN) along with the key idea of
Professor Forcing in training. A discriminator in GAN is jointly trained to
equalize the difference between real and predicted data. In AB subjective
listening test, the results show that the new approach is preferred over the
standard transfer learning with a CMOS improvement of 0.1. Sentence level
intelligibility tests show significant improvement in a pathological test set.
The GAN-trained new model is also more stable than the baseline to produce
better alignments for the Tacotron output.

本文提出了一种使用生成对抗网络和 Professor Forcing 概念训练的自回归模型，旨在减轻 TTS 中遭遇的曝光偏差问题，AB 测试结果表明该方法相较于传递学习得到了显著的 CMOS 0.1 的改进，语音测试集表现也有大幅度提升。