INTERSPEECH 2009
10th Annual Conference of the International Speech Communication Association

Brighton, United Kingdom
September 6-10, 2009

A Bayesian Approach to Non-Intrusive Quality Assessment of Speech

Petko N. Petkov (1), Iman S. Mossavat (2), W. Bastiaan Kleijn (1)

(1) KTH, Sweden
(2) Technische Universiteit Eindhoven, The Netherlands

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.

Full Paper

Bibliographic reference.  Petkov, Petko N. / Mossavat, Iman S. / Kleijn, W. Bastiaan (2009): "A Bayesian approach to non-intrusive quality assessment of speech", In INTERSPEECH-2009, 2875-2878.