Speech quality estimation has recently undergone a paradigm shift from
human-hearing expert designs to machine-learning models. However, current
models rely mainly on supervised learning, which is time-consuming and
expensive for label collection. To solve this problem, we propose VQScore, a
self-supervised metric for evaluating speech based on the quantization error of
a vector-quantized-variational autoencoder (VQ-VAE). The training of VQ-VAE
relies on clean speech; hence, large quantization errors can be expected when
the speech is distorted. To further improve correlation with real quality
scores, domain knowledge of speech processing is incorporated into the model
design. We found that the vector quantization mechanism could also be used for
self-supervised speech enhancement (SE) model training. To improve the
robustness of the encoder for SE, a novel self-distillation mechanism combined
with adversarial training is introduced. In summary, the proposed speech
quality estimation method and enhancement models require only clean speech for
training without any label requirements. Experimental results show that the
proposed VQScore and enhancement model are competitive with supervised
baselines. The code will be released after publication.

提出了一种基于自编码器的自监督度量方法（VQScore），用于评估语音质量，并结合领域知识进行模型设计，同时训练自主语音增强模型以改进编码器的鲁棒性。实验结果表明，该方法和增强模型与有监督基线模型具有竞争力。