A Bayesian hierarchical mixture of experts approach to estimate speech quality

S.I. Mossavat, O.D. Amft, B. Vries, de, P.N. Petkov, W.B. Kleijn

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

11 Citations (Scopus)
191 Downloads (Pure)


This paper demonstrates the potential of theoretically motivated learning methods in solving the problem of non-intrusive quality estimation for which the state-of-the-art is represented by ITU-T P.563 standard. To construct our estimator, we adopt the speech features from P.563, while we use a different mapping of features to form quality estimates. In contrast to P.563 which assumes distortion-classes to divide the feature space, our approach divides the feature space based on a clustering which is learned from the data using Bayesian inference. Despite using weaker modeling assumptions, we are still able to achieve comparable accuracy on predicting mean-opinion-scores with P.563. Our work suggests Bayesian model-evidence as an alternative metric to correlation-coefficient for determining the necessary number of experts for modeling the data.
Original languageEnglish
Title of host publicationSecond International Workshop on Quality of Multimedia Experience, IEEE Signal Processing Society, 2010, 21-23 June, TrondheiM
Publication statusPublished - 2010


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