While nonnegative matrix factorization based speech enhancing methods can improve signal to noise ratio (SNR) of recovered speech signal

these methods lead to the speech distortion

and thus degrade the performance of speaker verification system under noisy environment.This paper proposes a nonnegative matrix factorization with partial constrains (PCNMF)

with objective of enhancing the robustness of speaker verification system in presence of unknown and unstable noises.PCNMF constructs the speech and noise dictionaries while satisfying partition conditions.Considering that the speech dictionary generated by traditional speech training contains a little noise element

PCNMF generates speech dictionary using the spectra of pitch and their harmonics via mathematical model

and accordingly imitates the formant structure of human voice.The purpose is to guarantee the purity of speech dictionary.In addition

in order to alleviate the problem about the loss of the information of the noise sample

PCNMF performs framing operation and Short-Time Fourier Transform against the noise samples separated online

and then generates noise dictionary by means of linear combination of the spectrum frames of the noise samples.Our experiment takes unknown and unstable noises into account

demonstrating that the proposed PCNMF achieves significant improvement of robustness under various noise conditions.Particularly

the equal error rate of PCNMF is reduced by an average of 5.2% in comparison with the base-line (Multi-Condition system).