Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
A Hierarchical Bayesian Approach to Modeling Heterogeneity in Speech Quality Assessment
KTH, School of Electrical Engineering (EES), Sound and Image Processing.
KTH, School of Electrical Engineering (EES), Sound and Image Processing.
2012 (English)In: IEEE Transactions on Audio, Speech, and Language Processing, ISSN 1558-7916, E-ISSN 1558-7924, Vol. 20, no 1, 136-146 p.Article in journal (Refereed) Published
Abstract [en]

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.
Keyword [en]
Heterogeneity, hierarchical Bayesian, multi-task learning, non-intrusive, quality of service, single-ended, speech quality, variational inference
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-63239DOI: 10.1109/TASL.2011.2158421ISI: 000298325600016Scopus ID: 2-s2.0-81155126211OAI: oai:DiVA.org:kth-63239DiVA: diva2:484684
Funder
ICT - The Next Generation
Note

QC 20120127

Available from: 2012-01-27 Created: 2012-01-23 Last updated: 2017-12-08Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full textScopus

Search in DiVA

By author/editor
Petkov, Petko N.Kleijn, W. Bastiaan
By organisation
Sound and Image Processing
In the same journal
IEEE Transactions on Audio, Speech, and Language Processing
Other Electrical Engineering, Electronic Engineering, Information Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 49 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf