SPEAKER IDENTIFICATION SYSTEM BASED ON FEATURE JOINING, VECTOR QUANTIZATION AND DISTANCE NORMALIZATION
This paper presents a text-independent speaker identification system. Recognition scheme is based on vector quantization. In this article we provide modification of this method that allows for clustering of variable length vectors. Also, we propose two approaches. In first approach, a number of algorithms is used for feature extraction. The sets of various features present a single model. This feature joining is intended for more informative presentation of signal. In second approach, methods of distance normalization are introduced. Methods transform distances into matching scores using distances to all models. Distances from various speech segments are normalized in such way. Finally, we describe closed-set speaker identification experiments. The experiments conducted with 50 speakers. Weighted sum of identification rate in various conditions is used as performance measure. We show how new approaches contribute to the overall system performance. The achieved performance is 89,8 % while baseline system performance is 81,53 %.