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Conference Paper
Joint Bilinear Transformation Space Based Maximum a Posteriori Linear Regression Adaptation using Prior with Variance Function
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- Authors
- Hwa Jeon Song, Yun Keun Lee, Hyung Soon Kim
- Issue Date
- 2011-08
- Citation
- International Speech Communication Association (INTERSPEECH) 2011, pp.2577-2580
- Publisher
- ISCA
- Language
- English
- Type
- Conference Paper
- Abstract
- 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.
- KSP Keywords
- Bilinear Transformation, Space based, Tightly coupled, Variance function, linear regression, map-based, maximum a posteriori, prior distribution