Feature selection for a DTW-based speaker verification system

Speaker verification systems, in general, require 20 to 30 features as input for satisfactory verification. We show that this feature set can be optimised by appropriately choosing proper feature subset from the input feature set. This paper proposes a technique for optimisation of the feature sets, in an Dynamic Time Warping (DTW) based text-dependent speaker verification system, to improve false acceptance rate. The optimisation technique is based on the l-r algorithm. The proposed scheme is applied to study cepstrum coefficients and their first order orthogonal polynomial coefficients. Experiments are conducted on two data bases: French and Spanish. The results indicate that with the optimised feature set the performance of the system may improve but it is never degraded. Moreover, the speed of verification is significantly increased.