Robust principal component analysis based speaker verification under additive noise conditions

Abstract

Previous researches show that the approaches based on the total variability space (TVS) followed by Gaussian probabilistic linear discriminant analysis (GPLDA) work effectively for dealing with convolutional noise (such as channel noise) and can bring some degree of gains in term of accuracy under additive noisy environment as well. However they meet difficulty while many types of noises are unseen and non-stationary in real world. To address this issue, we introduce the robust principal component analysis (RPCA) into the TVS modeled speaker verification system, called RPCA-TVS, which regards the noise spectrum as the low-rank component and the speech spectrum as the sparse component in short-time Fourier transform (SFT) domain. The highlighting of this paper is to improve the robustness of speaker verification under additive noisy environment, especially in non-stationary and unseen noise conditions. For evaluating the performance, we designed and generated an additive noisy corpus, based on the TIMIT and NUST603-2014 database, using the NaFT tools with 12 types of noise samples deriving from NOISEX-92 and FREESOUND. Experimental results demonstrate that the proposed RPCA-TVS can achieve better performance than the competing methods at various signal-to-noise ratio (SNR) levels. Especially, RPCA-TVS reduces the equal error rate (EER) by 5.12 % in average than the multi-condition system under additive noise conditions at SNR = 8 dB.