Abstract: The current voiceprint recognition system has a good performance in a quiet environment,but in the variant noisy background,the performance will decrease sharply due to changes in training and application environment.To solve this problem,starting from the motivation of noise reduction in i-vector space,this paper proposes a robust noise adaption voiceprint recognition algorithm,which is i-vector partial least squares-auto encoder(IPLS-AE).IPLS-AE takes the partial least squares method to directly build the relationship between noisy i-vectors and clean i-vectors and then uses auto-encoder to describe the similarity between unknown and known noises.Experimental results illustrate that,compared to the typical i-vector maximum a posteriori(IMAP),IPLS-AE has a better compensation performance for various noises which are different types and signal-to-noise ratios(SNRs).For the known noise,the relative reduction of equal error rate(EER) and minimum detection cost function(minDCF) are 31.3% and 26.8%,and for the unknown noise,the relative reduction are 28.3% and 25.2%.The results show that the proposed IPLS-AE can effectively compensate for noise,and thereby improve the robustness of the system.