Rapid adaptation with linear combinations of rank-one matrices
Linear transforms are often used to adapt the acoustic models in speech recognition systems. When there is very little (5–10 sees.) acoustic data adaptation suffers from unreliable parameter estimation. Typically this problem is handled by imposing a diagonal or block diagonal structure on the transform. This paper proposes using transforms that are linear combinations of rank-one matrices. This approach is applied to the adaptation of the Gaussian means, Gaussian covariances and the acoustic features. Experimental results with varying amounts of adaptation data indicate that for the same number of parameters, our new parameterization performs significantly better than simpler transform parameterizations (diagonal and/or block-diagonal).