Ensemble of Deep Neural Network Models for MOS Prediction
Marie Kunesová, Jindrich Matousek, Jan Lehecka, Jan Svec, Josef Michálek, Daniel Tihelka, Martin Bulín, Zdenek Hanzlícek
Abstract
Automatic evaluation of the quality of synthetic speech has the potential to serve as a cheaper and less time-consuming alternative to standard listening tests. In this paper, we present our contribution to the ongoing research: a system for automatic prediction of the mean opinion score (MOS) given by human listeners. The system was specifically developed for the recent VoiceMOS Challenge. Following the success of fusion systems in similar challenges, our contribution is an ensemble that interpolates the outputs of seven different models: four different wav2vec models, a CNN-RNN model, QuartzNet, and the LDNet baseline. During the VoiceMOS challenge, our system achieved the second-best utterance-level MSE of 0.171 and ranged from 2nd to 8th place among all 22 participating teams in terms of other evaluation metrics.
BibTeX
@inproceedings{icassp2023_ensembleofdeepne,
title = {Ensemble of Deep Neural Network Models for MOS Prediction},
author = {Marie Kunesová and Jindrich Matousek and Jan Lehecka and Jan Svec and Josef Michálek and Daniel Tihelka and Martin Bulín and Zdenek Hanzlícek and Markéta Rezácková},
booktitle = {ICASSP 2023},
year = {2023}
}