A discriminative training algorithm for VQ-based speaker identification

作     者：He, JL Liu, L Palm, G 

作者机构：Univ Ulm Abt Neuroinformat D-89069 Ulm Germany 

出 版 物：《IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING》 (IEEE Trans Speech Audio Process)

年 卷 期：1999年第7卷第3期

页      面：353-356页

核心收录：

主　　题：neural networks speaker identification training algorithms vector quantization 

摘      要：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.