Bilinear Model-Based Maximum Likelihood Linear Regression Speaker Adaptation Framework

This letter proposes a novel framework for speaker adaptation, using bilinear model-based maximum likelihood linear regression (MLLR) method. First, a set of speaker models is decomposed into the style factor identified as each speaker´s characteristics and the common content factor across the speakers, by the bilinear model. Then, using some adaptation data from a new speaker, the speaker-specific model is generated by properly adjusting the dimensionality of the content factor and estimating a new style factor simultaneously. Experimental results show that the proposed framework outperforms MLLR with fewer number of parameters to be estimated.