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

Autor:	Choi, Yeunju, Jung, Youngmoon, Suh, Youngjoo, Kim, Hoirin
Rok vydání:	2020
Předmět:	Electrical Engineering and Systems Science - Audio and Speech Processing Computer Science - Machine Learning Computer Science - Sound
Zdroj:	IEEE Access, vol. 10, pp. 52621 - 52629, 2022
Druh dokumentu:	Working Paper
DOI:	10.1109/ACCESS.2022.3175810
Popis:	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. Comment: 9 pages, 5 figures, 4 tables
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2011.01174 Zobrazit plný text záznamu View this record from Arxiv