  In this paper, we evaluate feature extraction models for predicting speech
quality. We also propose a model architecture to compare embeddings of
supervised learning and self-supervised learning models with embeddings of
speaker verification models to predict the metric MOS. Our experiments were
performed on the VCC2018 dataset and a Brazilian-Portuguese dataset called
BRSpeechMOS, which was created for this work. The results show that the Whisper
model is appropriate in all scenarios: with both the VCC2018 and BRSpeech- MOS
datasets. Among the supervised and self-supervised learning models using
BRSpeechMOS, Whisper-Small achieved the best linear correlation of 0.6980, and
the speaker verification model, SpeakerNet, had linear correlation of 0.6963.
Using VCC2018, the best supervised and self-supervised learning model,
Whisper-Large, achieved linear correlation of 0.7274, and the best model
speaker verification, TitaNet, achieved a linear correlation of 0.6933.
Although the results of the speaker verification models are slightly lower, the
SpeakerNet model has only 5M parameters, making it suitable for real-time
applications, and the TitaNet model produces an embedding of size 192, the
smallest among all the evaluated models. The experiment results are
reproducible with publicly available source-code1 .
