@inproceedings{diener2023plcmos,
  title        = {PLCMOS - a data-driven non-intrusive metric for the evaluation of packet loss
    concealment algorithms},
  author       = {Diener, Lorenz and Sootla, Sten and Purin, Marju and Saabas, Ando and Aichner,
    Robert and Cutler, Ross},
  year         = 2023,
  month        = aug,
  booktitle    = {{INTERSPEECH} 2023 - 23nd Annual Conference of the International Speech
    Communication Association},
  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.},
  doi          = {10.21437/Interspeech.2023-1532},
  code         = {https://aka.ms/plcmos},
  url          = {https://halcy.de/cites/pdf/diener2023plcmos.pdf},
  poster       = {https://halcy.de/cites/pdf/diener2023plcmos_poster.pdf},
}