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Speech signals are often affected by additive noise and distortion which can degrade the perceived quality and intelligibility of the signal. We present a new measure, NISA, for estimating the quality and intelligibility of speech degraded by additive noise and distortions associated with telecommunications networks, based on a data driven framework of feature extraction and tree based regression...
We present NISQ, a data-driven non-intrusive speech quality measure that has been trained to predict the PESQ score for a given speech signal. NISQ is based on feature extraction and a binary tree regression based model. A training method using the intrusive PESQ algorithm to automatically label large quantities of speech data is presented and utilized. Our method is shown to predict PESQ with an...
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