  Although recent neural text-to-speech (TTS) systems have achieved
high-quality speech synthesis, there are cases where a TTS system generates
low-quality speech, mainly caused by limited training data or information loss
during knowledge distillation. Therefore, we propose a novel method to improve
speech quality by training a TTS model under the supervision of perceptual
loss, which measures the distance between the maximum possible speech quality
score and the predicted one. We first pre-train a mean opinion score (MOS)
prediction model and then train a TTS model to maximize the MOS of synthesized
speech using the pre-trained MOS prediction model. The proposed method can be
applied independently regardless of the TTS model architecture or the cause of
speech quality degradation and efficiently without increasing the inference
time or model complexity. The evaluation results for the MOS and phone error
rate demonstrate that our proposed approach improves previous models in terms
of both naturalness and intelligibility.
