Signal Bias Removal based GMM for robust speaker recognition

In this paper, we focus on the combined method of SBR and GMM-UBM and its capacity for detection and robustness of speaker recognition. While each method has achieved improvements independent of each other in an orthogonal field, both methods have a similar framework. The proposed Signal Bias Removal based GMM (SBR-GMM) executes the minimization of the environmental variation on mismatched condition by removing the bias of the distorted input signal and the adaptation of the speaker-dependent characteristics from the clean, text independent and speaker independent background GMM. In our experiments, we compared the closed-set speaker identification for conventional CMS and the proposed method respectively on TIMIT and NTIMIT database. Particularly in the third set of experiments on NTIMIT, compared to CMS, we were able to improve the recognition rate by 27.4% using the robust feature.