LE-SSL-MOS: Self-Supervised Learning MOS Prediction with Listener Enhancement

Zili Qi, Xinhui Hu, Wangjin Zhou, Sheng Li, Hao Wu, Jian Lu, Xinkang Xu

Abstract: Recently, researchers have shown an increasing interest in automatically predicting the subjective evaluation for speech synthesis systems. This prediction is a challenging task, especially on the out-of-domain test set. In this paper, we proposed a novel fusion model for MOS prediction that combines both supervised and unsupervised approaches. In the supervised aspect, we developed a SSL-based predictor called LE-SSL-MOS. The LE-SSL-MOS utilizes pre-trained self-supervised learning models and further improves prediction accuracy by utilizing the opinion scores of each utterance in the listener enhancement branch. In the unsupervised aspect, two steps are contained: one is that we fine-tuned unit language model (ULM) using highly-intelligible domain data to improve the correlation of an unsupervised metric SpeechLMScore. Another is that we utilized ASR confidence as a new metric with the help of ensemble learning. To the best of our knowledge, this is the first architecture that fuses supervised and unsupervised methods for MOS prediction.With these approaches, our experimental results on the VoiceMOS Challenge 2023 show that LE-SSL-MOS performs better than the baseline. Our fusion system achieved an absolute improvement of 13 % over LE-SSL-MOS on the noisy and enhanced speech track. And our system ranked 1st and 2 nd respectively in the French speech synthesis track and the noisy and enhanced speech track of the challenge.