A Bayesian hierarchical mixture of experts approach to estimate speech quality
2010 (English)In: 2010 2nd International Workshop on Quality of Multimedia Experience, 2010, Vol. QoMEX 2010 - Proceedings, 200-205 p.Conference paper (Refereed)
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.
Place, publisher, year, edition, pages
2010. Vol. QoMEX 2010 - Proceedings, 200-205 p.
Bayesian, Mixture of experts, Non-intrusive, Objective quality assessment, Output-based, P.563, Single-ended, Speech, Variational, Inference engines, Mixtures, Bayesian networks
Computer and Information Science
IdentifiersURN: urn:nbn:se:kth:diva-36317DOI: 10.1109/QOMEX.2010.5516203ScopusID: 2-s2.0-77955764588ISBN: 9781424469604OAI: oai:DiVA.org:kth-36317DiVA: diva2:430670
2010 2nd International Workshop on Quality of Multimedia Experience, QoMEX 2010; Trondheim
QC 201107122011-07-122011-07-112011-07-12Bibliographically approved