MAXIMUM LIKELIHOOD TRAINING OF BASES FOR RAPID ADAPTATION
Adaptation using linear transforms is well known to significantly improve the performance of speech recognition systems. Restricting the transforms to lie in a subspace obtained at training time has been shown to be useful in achieving good performance even with few seconds of adaptation data. This paper addresses the issue of how the subspace is learned. Algorithms for maximum likelihood (ML) estimation of the basis (which defines the subspace) are given. Our experimental results suggest that ML estimation of bases provides better error rate performance over bases learned using Principal Component Analysis (PCA). Moreover, the gains are greater for smaller bases, which are practically important since they can be used at test time with smaller memory and computational overhead. We also explore adaptation with joint mean-feature space transforms.