In the Maximum Likelihood Linear Regression (MLLR) framework

this paper proposes two fast speaker adaptation approaches

which are called Speaker Adaptation using Triple Diagonal matrices in the log-spectral domain (SATD) and Speaker Adaptation using Shared Block Diagonal matrices (SASBD) in the cepstral domain

respectively.Based on some prior knowledge

the proposed approaches utilize fewer parameters to describe the variation between speakers

and thus fewer adaptation data are needed to give robust estimation.Experimental results in both the whole-word-modeled isolated word recognition system and the isolated word recognition system using triphones as modeling units show that the proposed approaches can provide faster performance than the traditional MLLR approaches.