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In this paper, we present an update to the NISQA speech quality predictionmodel that is focused on distortions that occur in communication networks. Incontrast to the previous version, the model is trained end-to-end and thetime-dependency modelling and time-pooling is achieved through a Self-Attentionmechanism. Besides overall speech quality, the model also predicts the fourspeech quality dimensions Noisiness, Coloration, Discontinuity, and Loudness,and in this way gives more insight into the cause of a quality degradation.Furthermore, new datasets with over 13,000 speech files were created fortraining and validation of the model. The model was finally tested on a new,live-talking test dataset that contains recordings of real telephone calls.Overall, NISQA was trained and evaluated on 81 datasets from different sourcesand showed to provide reliable predictions also for unknown speech samples. Thecode, model weights, and datasets are open-sourced.

NISQA: A Deep CNN-Self-Attention Model for Multidimensional Speech Quality Prediction with Crowdsourced Datasets