  The objective speech quality assessment is usually conducted by comparing
received speech signal with its clean reference, while human beings are capable
of evaluating the speech quality without any reference, such as in the mean
opinion score (MOS) tests. Non-intrusive speech quality assessment has
attracted much attention recently due to the lack of access to clean reference
signals for objective evaluations in real scenarios. In this paper, we propose
a novel non-intrusive speech quality measurement model, MetricNet, which
leverages label distribution learning and joint speech reconstruction learning
to achieve significantly improved performance compared to the existing
non-intrusive speech quality measurement models. We demonstrate that the
proposed approach yields promisingly high correlation to the intrusive
objective evaluation of speech quality on clean, noisy and processed speech
data.
