Eigenspace-based maximum a posteriori linear regression for rapid speaker adaptation

Abstract

We present an eigenspace-based approach toward prior density selection for the MAPLR framework. The proposed eigenspace-based MAPLR approach was developed by introducing a priori knowledge analysis on the training speakers via probabilistic principal component analysis (PPCA), so as to construct an eigenspace for speaker-specific full regression matrices as well as to derive a set of bases called eigen-matrices. The priors of MAPLR transformations for each outside speaker are then chosen in the space spanned by the first K eigen-matrices. By incorporating the PPCA model into the MAPLR scheme, the number of free parameters in choosing the priors can be effectively reduced, while the underlying structure of the acoustic space as well as the precise modeling of the inter-dimensional correlation among the model parameters can be well preserved. Both supervised and unsupervised adaptation experiments showed that the proposed approach significantly outperformed the conventional maximum likelihood linear regression (MLLR) approach using either diagonal or full regression matrices.