12th Annual Conference of the International Speech Communication Association

Florence, Italy
August 27-31. 2011

Joint Bilinear Transformation Space Based Maximum a posteriori Linear Regression Adaptation Using Prior with Variance Function

Hwa Jeon Song (1), Yunkeun Lee (1), Hyung Soon Kim (2)

(1) ETRI, Korea
(2) Pusan National University, Korea

This paper proposes a new joint maximum a posteriori linear regression (MAPLR) adaptation using single prior distribution with a variance function in bilinear transformation space (BITS). There are two indirect adaptation methods based on the linear transformation in BITS and these are tightly coupled by joint MAP-based estimation. The proposed method not only has the scalable parameters but also is based on only one prior distribution, unlike the conventional joint MAP-MAPLR method with two priors. Experimental results, especially for small amount of adaptation data, show the synergy between two indirect BITS-based methods over other methods.

Full Paper

Bibliographic reference.  Song, Hwa Jeon / Lee, Yunkeun / Kim, Hyung Soon (2011): "Joint bilinear transformation space based maximum a posteriori linear regression adaptation using prior with variance function", In INTERSPEECH-2011, 2577-2580.