Abstract: Current speaker subspace based adaptation method cannot obtain the best speaker subspace. A speaker adaptation method based on matching pursuit was proposed to adress this problem. Speaker adaptation was viewed as the sparse decomposition of a high dimensional speaker supervector with an over-complete dictionary, which was constructed by eigenvoices and reference speaker supervectors. Through an efficient iteratively optimization process, the best speaker dependent subspace was determined in a maximum a posterior way. A redundant bases removing mechanism was introduced to ensure the numeric stability and new speaker's coordinate was obtained through a fast recurrence algorithm. Superised speaker adaptation on a Chinese continuous speech recognition system show that compared with the eigenvoice and reference speaker weighting methods, the recognition accuracy was improved by relatively 1.9%