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In this chapter, the single-ended speech quality prediction model NISQA is presented. It is based on the neural network architectures described in the previous chapters. Overall, the model is trained and evaluated on a wide variety of 78 different datasets.
The NISQA Corpus includes more than 14,000 speech samples with simulated (e.g. codecs, packet-loss, background noise) and live (e.g. mobile phone, Zoom, Skype, WhatsApp) conditions. Each file is labelled with subjective ratings of the overall quality and the quality dimensions Noisiness, Coloration, Discontinuity, and Loudness. In total, it contains more .
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NISQA is a deep learning model/framework for speech quality prediction. The NISQA model weights can be used to predict the quality of a speech sample that has been sent through .