In this paper

a new speaker adaptation method—codebook-based speaker adaptation

which could combine the advantages of transform method with Bayes adaptive learning method appropriately

is presented.Not only can the speaker adaptation system improve its performance for small amount of adaptation data

but it can also approach asymptotically matched-condition performance with increasing number of adaptation data.The adaptation process can be divided into two stages.In the first stage

for approximating the acoustic parameters of a target speaker

the linear combination of lots of reference speaker's codebooks is proposed.An effective algorithm based on Rosen gradient projection method is developed to count the weight of each codebook in the linear combination.In the second stage

the combination of codebooks is used as the prior probability

then Bayes adaptive learning method is used to learn the exact value of the target speaker's codebook as more adaptation data are gathered.Thus incremental speaker adaptation can be achieved.As an illustration

this method is applied to a speaker independent continuous speech recognition system for the Chinese language.A series of comparative experiments were conducted to evaluate the performance of the proposed method.The results have shown it is quite promising.