Despite the huge successes made in neutral TTS, content-leakage remains a
challenge. In this paper, we propose a new input representation and simple
architecture to achieve improved prosody modeling. Inspired by the recent
success in the use of discrete code in TTS, we introduce discrete code to the
input of the reference encoder. Specifically, we leverage the vector quantizer
from the audio compression model to exploit the diverse acoustic information it
has already been trained on. In addition, we apply the modified MLP-Mixer to
the reference encoder, making the architecture lighter. As a result, we train
the prosody transfer TTS in an end-to-end manner. We prove the effectiveness of
our method through both subjective and objective evaluations. We demonstrate
that the reference encoder learns better speaker-independent prosody when
discrete code is utilized as input in the experiments. In addition, we obtain
comparable results even when fewer parameters are inputted.

本文提出了一种新的输入表示和简单体系结构，通过引入离散码到参考编码器的输入，利用音频压缩模型的向量量化器来实现改进的韵律建模。在主观和客观评估中证明了该方法的有效性，即离散码作为输入时参考编码器学习更好的说话人无关韵律。