Spatial correlation transformation based on minimum covariance

In speech recognition, acoustic units are highly related. Different from some adaptation methods, such as reference speaker weighting (RSW) and eigenvoice, the correlation between different acoustic units in the feature space, which is called spatial correlation, focuses on the correlation information among different acoustic units of the same speaker. In this paper, a novel scheme using spatial correlation is proposed. In speech recognition system, with the spatial correlation information, the refined acoustic models are trained, and the transformation matrices are determined based on minimum covariance criteria. Experiments of this new algorithm show a significant improvement on speaker independent recognition systems.