A Bayesian Approach to Non-Intrusive Quality Assessment of Speech
2009 (English)In: INTERSPEECH 2009: 10TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2009, BAIXAS: ISCA-INST SPEECH COMMUNICATION ASSOC , 2009, 2875-2878 p.Conference paper (Refereed)
A Bayesian approach to non-intrusive quality assessment of narrow-band speech is presented. The speech features used to assess quality are the sample mean and variance of band-powers evaluated from the temporal envelope in the channels of an auditory filter-bank. Bayesian multivariate adaptive regression splines (BMARS) is used to map features into quality ratings. The proposed combination of features and regression method leads to a high performance quality assessment algorithm that learns efficiently from a small amount of training data and avoids overfitting. Use of the Bayesian approach also allows the derivation of credible intervals on the model predictions, which provide a quantitative measure of model confidence and can be used to identify the need for complementing the training databases.
Place, publisher, year, edition, pages
BAIXAS: ISCA-INST SPEECH COMMUNICATION ASSOC , 2009. 2875-2878 p.
non-intrusive quality assessment, modulation spectrum, Bayesian MARS
Computer and Information Science General Language Studies and Linguistics
IdentifiersURN: urn:nbn:se:kth:diva-29888ISI: 000276842801251ScopusID: 2-s2.0-70450209993OAI: oai:DiVA.org:kth-29888DiVA: diva2:399017
10th INTERSPEECH 2009 Conference, Brighton, ENGLAND, SEP 06-10, 2009
QC 201102212011-02-212011-02-172011-02-21Bibliographically approved