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Speech quality assessment has been a critical component in many voicecommunication related applications such as telephony and online conferencing.Traditional intrusive speech quality assessment requires the clean reference ofthe degraded utterance to provide an accurate quality measurement. Thisrequirement limits the usability of these methods in real-world scenarios. Onthe other hand, non-intrusive subjective measurement is the ``golden standard"in evaluating speech quality as human listeners can intrinsically evaluate thequality of any degraded speech with ease. In this paper, we propose a novelend-to-end model structure called Convolutional Context-Aware Transformer(CCAT) network to predict the mean opinion score (MOS) of human raters. Weevaluate our model on three MOS-annotated datasets spanning multiple languagesand distortion types and submit our results to the ConferencingSpeech 2022Challenge. Our experiments show that CCAT provides promising MOS predictionscompared to current state-of-art non-intrusive speech assessment models withaverage Pearson correlation coefficient (PCC) increasing from 0.530 to 0.697and average RMSE decreasing from 0.768 to 0.570 compared to the baseline modelon the challenge evaluation test set.

CCATMos: Convolutional Context-aware Transformer Network for Non-intrusive Speech Quality Assessment