Learning to Maximize Speech Quality Directly Using MOS Prediction for Neural Text-to-Speech

TL;DR

This paper introduces a novel training method for neural TTS systems that directly optimizes speech quality by predicting MOS scores, leading to improved naturalness and intelligibility without increasing complexity.

Contribution

The authors propose a MOS-based perceptual loss training approach for TTS models, which is independent of architecture and enhances speech quality effectively.

Findings

Improved MOS scores indicating higher speech naturalness.

Enhanced phoneme error rates showing better intelligibility.

Method is applicable across different TTS architectures.

Abstract

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.