A discriminative training algorithm for VQ-based speaker identification

A novel method, referred to as group vector quantization (GVQ), is proposed to train VQ codebooks for closed-set speaker identification. In GVQ training, speaker codebooks are optimized for vector groups rather than for individual vectors. An evaluation experiment has been conducted to compare the codebooks trained by the Linde-Buzo-Grey (LBG), the learning vector quantization (LVQ), and the GVQ algorithms. It is shown that the frame scores from the GVQ trained codebooks are less correlated, therefore, the sentence level speaker identification rate increases more quickly with the length of test sentences.