Title :
A discriminative training algorithm for VQ-based speaker identification
Author :
He, Jialong ; Liu, Li ; Palm, Gunther
Author_Institution :
Abteilung Neuroinf., Ulm Univ., Germany
fDate :
5/1/1999 12:00:00 AM
Abstract :
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
Keywords :
learning (artificial intelligence); speaker recognition; speech coding; vector quantisation; GVQ algorithm; LBG algorithm; LVQ algorithm; Linde-Buzo-Grey algorithm; VQ codebooks; VQ-based speaker identification; closed-set speaker identification; discriminative training algorithm; frame scores; group vector quantization; learning vector quantization algorithm; sentence level speaker identification rate; speaker codebooks; Density functional theory; Error analysis; Helium; Neural networks; Prototypes; Speaker recognition; Speech; Testing; Training data; Vector quantization;
Journal_Title :
Speech and Audio Processing, IEEE Transactions on