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Speech quality assessment is a problem for every researcher working on modelsthat produce or process speech. Human subjective ratings, the gold standard inspeech quality assessment, are expensive and time-consuming to acquire in aquantity that is sufficient to get reliable data, while automated objectivemetrics show a low correlation with gold standard ratings. This paper presentsPLCMOS, a non-intrusive data-driven tool for generating a robust, accurateestimate of the mean opinion score a human rater would assign an audio filethat has been processed by being transmitted over a degraded packet-switchednetwork with missing packets being healed by a packet loss concealmentalgorithm. Our new model shows a model-wise Pearson's correlation of ~0.97 andrank correlation of ~0.95 with human ratings, substantially above all otheravailable intrusive and non-intrusive metrics. The model is released as an ONNXmodel for other researchers to use when building PLC systems.

PLCMOS -- a data-driven non-intrusive metric for the evaluation of packet loss concealment algorithms