EMAP-based speaker adaptation with robust correlation estimation

We propose a method to enhance the performance of the extended maximum a posteriori (EMAP) estimation using the probabilistic principal component analysis (PPCA). PPCA is used to robustly estimate the correlation matrix among separate hidden Markov model (HMM) parameters. The correlation matrix is then applied to the EMAP scheme for speaker adaptation. PPCA is efficient to compute, and shows better performance compared to the method previously used for EMAP. Through various experiments on continuous digit recognition, it is shown that the EMAP approach based on the PPCA gives enhanced performance especially for a small amount of adaptation data