Self-Supervised Speech Quality Estimation and Enhancement Using Only Clean Speech

arXiv:2402.16321v1 Announce Type: cross
Abstract: 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. …

abstract arxiv collection cs.ai cs.lg cs.sd current designs eess.as expert hearing human machine paradigm quality shift solve speech supervised learning type