Abstract: An efficient base selection method for eigenvoice speaker adaptation is proposed. With an appropriate regularization term, the set of eigenvoices which represent the unknown speaker are automatically chosen through a mathematic optimization process. Three regularization terms, namely l1, l2 and elastic net regularization, are discussed and the corresponding optimization methods are presented. With an l1 regularization, a sparse solution of the speaker factor is obtained and the non-zero terms correspond to the selected eigenvoices. Using l2 regularization, the robustness of the speaker factor can be improved in case of limited adaptation data and the requirement of the prior selection of speaker subspace can be relaxed. The elastic net regularization combines l1 and l2 regularization. Supervised speaker adaptation experimental results show that when the adaptation data are limited (less than 10s) the regularization method can improve the performance relatively by 1%～2% compared with the best results of the eigenvoice method. Among the three regularization terms, l1 regularization is slightly better than the l2 regularization, and performance can be further improved when the elastic net regularization is used.