The ZevoMOS entry to VoiceMOS Challenge 2022

TL;DR

This paper presents ZevoMOS, a speech quality assessment system that finetunes pretrained SSL models with classification and regression tasks, combining multiple features to predict MOS scores for voice quality evaluation.

Contribution

The paper introduces a novel two-step finetuning approach for SSL speech models and combines multiple features for improved voice MOS prediction in the VoiceMOS Challenge 2022.

Findings

Achieved 14th place in system-level SRCC

Achieved 9th place in utterance-level MSE

Demonstrated effectiveness of combining SSL embeddings with confidence scores

Abstract

This paper introduces the ZevoMOS entry to the main track of the VoiceMOS Challenge 2022. The ZevoMOS submission is based on a two-step finetuning of pretrained self-supervised learning (SSL) speech models. The first step uses a task of classifying natural versus synthetic speech, while the second step's task is to predict the MOS scores associated with each training sample. The results of the finetuning process are then combined with the confidence scores extracted from an automatic speech recognition model, as well as the raw embeddings of the training samples obtained from a wav2vec SSL speech model. The team id assigned to the ZevoMOS system within the VoiceMOS Challenge is T01. The submission was placed on the 14th place with respect to the system-level SRCC, and on the 9th place with respect to the utterance-level MSE. The paper also introduces additional evaluations of the intermediate results.