Robust feature front-end for speaker identification

One important challenge for speaker identification (SID) system is sustained performance in diverse conditions. This study presents a novel front-end feature extraction method for SID in clean, noisy, and channel-mismatched acoustic conditions. To address the problem, the perceptual minimum variance distortionless response (PMVDR) feature is employed. While PMVDR has been successfully used for noisy ASR, it has not been considered for SID. We also incorporate longer temporal speaker knowledge based on the shifted delta cepstral (SDC) algorithm. The evaluation over YOHO and another new diversified Robust Open-Set Speaker Identification (ROSSI) database show that both PMVDR and the union with SDC can improve performance significantly. Compared with traditional feature extraction, PMVDR and PMVDR-SDC always give improvement across diverse adverse conditions. Also, PMVDR-SDC can contribute additional improvement in the presence of noise and channel mismatch.