  Human judgments obtained through Mean Opinion Scores (MOS) are the most
reliable way to assess the quality of speech signals. However, several recent
attempts to automatically estimate MOS using deep learning approaches lack
robustness and generalization capabilities, limiting their use in real-world
applications. In this work, we present a novel framework, NORESQA-MOS, for
estimating the MOS of a speech signal. Unlike prior works, our approach uses
non-matching references as a form of conditioning to ground the MOS estimation
by neural networks. We show that NORESQA-MOS provides better generalization and
more robust MOS estimation than previous state-of-the-art methods such as
DNSMOS and NISQA, even though we use a smaller training set. Moreover, we also
show that our generic framework can be combined with other learning methods
such as self-supervised learning and can further supplement the benefits from
these methods.
