The VoiceMOS Challenge 2023: Zero-shot Subjective Speech Quality Prediction for Multiple Domains
Abstract
We present the second edition of the VoiceMOS Challenge, a scientific event that aims to promote the study of automatic prediction of the mean opinion score (MOS) of synthesized and processed speech. This year, we emphasize real-world and challenging zero-shot out-of-domain MOS prediction with three tracks for three different voice evaluation scenarios. Ten teams from industry and academia in seven different countries participated. Surprisingly, we found that the two sub-tracks of French text-to-speech synthesis had large differences in their predictability, and that singing voice-converted samples were not as difficult to predict as we had expected. Use of diverse datasets and listener information during training appeared to be successful approaches.
Keywords
Cite
@article{arxiv.2310.02640,
title = {The VoiceMOS Challenge 2023: Zero-shot Subjective Speech Quality Prediction for Multiple Domains},
author = {Erica Cooper and Wen-Chin Huang and Yu Tsao and Hsin-Min Wang and Tomoki Toda and Junichi Yamagishi},
journal= {arXiv preprint arXiv:2310.02640},
year = {2023}
}
Comments
Accepted to ASRU 2023