Robust speaker identification using combined feature selection and missing data recognition

Missing data techniques have been recently applied to speaker recognition to increase performance in noisy environments. The drawback of these techniques is the vulnerability of the recognizer to errors in the classification of time-frequency points as corrupt or reliable. In this paper we propose the combination of missing data processing and feature selection to reduce these errors. The formation of a set of speaker discriminative features allows time-frequency reliability masks to be refined via the removal of the non-discriminative frequency sub-bands. The reduced set is selected dynamically using multi-condition training and an estimate of the global SNR allowing for efficient top-down processing. Experimental results show that the combined technique achieves significant improvement over traditional bottom-up processing thus demonstrating the validity of the approach.