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arXiv · 2305.15127

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

Lorenz Diener·Marju Purin·Sten Sootla·Ando Saabas·Robert Aichner·Ross Cutler
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Abstract

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.

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Lorenz Diener, Marju Purin, Sten Sootla, Ando Saabas, Robert Aichner, Ross Cutler. 2023-05-24. PLCMOS -- a data-driven non-intrusive metric for the evaluation of packet loss concealment algorithms. https://arxiv.org/abs/2305.15127

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