  Speech quality assessment is a problem for every researcher working on models
that produce or process speech. Human subjective ratings, the gold standard in
speech quality assessment, are expensive and time-consuming to acquire in a
quantity that is sufficient to get reliable data, while automated objective
metrics show a low correlation with gold standard ratings. This paper presents
PLCMOS, a non-intrusive data-driven tool for generating a robust, accurate
estimate of the mean opinion score a human rater would assign an audio file
that has been processed by being transmitted over a degraded packet-switched
network with missing packets being healed by a packet loss concealment
algorithm. Our new model shows a model-wise Pearson's correlation of ~0.97 and
rank correlation of ~0.95 with human ratings, substantially above all other
available intrusive and non-intrusive metrics. The model is released as an ONNX
model for other researchers to use when building PLC systems.
