Bilinear transformation space-based maximum likelihood linear regression frameworks

This paper proposes two types of bilinear transformation spacebased speaker adaptation frameworks. In training session, transformation matrices for speakers are decomposed into the style factor for speakers’ characteristics and orthonormal basis of eigenvectors to control dimensionality of the canonical model by the singular value decomposition-based algorithm. In adaptation session, the style factor of a new speaker is estimated, depending on what kind of proposed framework is used. At the same time, the dimensionality of the canonical model can be reduced by the orthonormal basis from training. Moreover, both maximum likelihood linear regression (MLLR) and eigenspacebased MLLR are identified as special cases of our proposed methods. Experimental results show that the proposed methods are much more effective and versatile than other methods.