Fast speaker adaptation using triple diagonal and shared block diagonal transform matrices

This paper proposes two fast and effective adaptation algorithms, which are called SATD and SASBD respectively. The two algorithms are implemented in the MLLR frame and the transform matrices have constrained forms. SATD uses triple diagonal matrices to describe the mismatch between speakers and the acoustic model in the log-spectral domain and the matrices can be transformed into the cepstral domain to adjust the acoustic model. SASBD is different from the traditional block-diagonal MLLR and shares the three transformations of basic MFCC and dynamic features with one matrix. Moreover, both algorithms provide multiple choices for the biases. Experiments are extensively implemented and the results prove the advantages of SATD and SASBD over traditional MLLR.