Nonintrusive speech quality estimation using Gaussian mixture models

An algorithm for nonintrusive speech quality estimation based on Gaussian mixture models (GMMs) is presented. GMMs are used to form an artificial reference model of the behavior of features of undegraded speech. Consistency measures between the degraded speech signal and the reference model serve as indicators of speech quality. Consistency values are mapped to an objective speech quality score using a multivariate adaptive regression splines function. When tested on unseen data, the proposed algorithm generally outperforms ITU-T standard P.563, which is the current "state-of-the-art" algorithm. The algorithm computes objective quality scores roughly twice as fast as P.563.