A Hierarchical Bayesian Approach to Modeling Heterogeneity in Speech Quality Assessment
2012 (English)In: IEEE Transactions on Audio, Speech, and Language Processing, ISSN 1558-7916, Vol. 20, no 1, 136-146 p.Article in journal (Refereed) Published
The development of objective speech quality measures generally involves fitting a model to subjective rating data. A typical data set comprises ratings generated by listening tests performed in different languages and across different laboratories. These factors as well as others, such as the sex and age of the talker, influence the subjective ratings and result in data heterogeneity. We use a linear hierarchical Bayes (HB) structure to account for heterogeneity. To make the structure effective, we develop a variational Bayesian inference for the linear HB structure that approximates not only the posterior over the model parameters, but also the model evidence. Using the approximate model evidence we are able to study and exploit the heterogeneity inducing factors in the Bayesian framework. The new approach yields a simple linear predictor with state-of-the-art predictive performance. Our experiments show that the new method compares favorably with systems based on more complex predictor structures such as ITU-T recommendation P.563, Bayesian MARS, and Gaussian processes.
Place, publisher, year, edition, pages
2012. Vol. 20, no 1, 136-146 p.
Heterogeneity, hierarchical Bayesian, multi-task learning, non-intrusive, quality of service, single-ended, speech quality, variational inference
Other Electrical Engineering, Electronic Engineering, Information Engineering
IdentifiersURN: urn:nbn:se:kth:diva-63239DOI: 10.1109/TASL.2011.2158421ISI: 000298325600016ScopusID: 2-s2.0-81155126211OAI: oai:DiVA.org:kth-63239DiVA: diva2:484684
FunderICT - The Next Generation
QC 201201272012-01-272012-01-232013-04-11Bibliographically approved