Objective Speech Quality Assessment Using Gaussian Mixture Models
نویسندگان
چکیده
Objective speech quality assessment algorithms provide low-cost and online monitoring of voice calls, replacing costly and timeconsuming subjective listening tests. We propose a novel approach to objective speech quality measurements using Gaussian mixture models (GMMs). A large pool of perceptual distortion features is extracted from speech files and multivariate adaptive regression splines (MARS) is used to sift out the most relevant variables from the pool. The five most salient variables are used to construct good GMM estimators of subjective listening quality. Simulation results show that this novel approach outperforms the state-of-theart objective measurement algorithm, PESQ.
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