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 .

翻译：本文评估了用于预测语音质量的特征提取模型，并提出了一种模型架构，用于比较监督学习和自监督学习模型的嵌入表征与说话人验证模型的嵌入表征在预测MOS指标上的表现。实验基于VCC2018数据集以及为本研究所创建的巴西葡萄牙语数据集BRSpeechMOS。结果表明，Whisper模型在所有场景中均表现优异：无论是使用VCC2018数据集还是BRSpeechMOS数据集。在基于BRSpeechMOS数据集的监督与自监督学习模型中，Whisper-Small取得了0.6980的最佳线性相关系数，而说话人验证模型SpeakerNet的线性相关系数为0.6963。使用VCC2018数据集时，表现最优的监督与自监督学习模型Whisper-Large的线性相关系数为0.7274，最佳说话人验证模型TitaNet的线性相关系数为0.6933。尽管说话人验证模型的性能略低，但SpeakerNet模型仅含5M参数，使其适用于实时应用，而TitaNet模型生成的嵌入表征大小为192，是所评估模型中尺寸最小的。实验结果可通过公开的源代码重现¹。