TY - GEN
T1 - Maximum model distance discriminative training for text-independent speaker verification
AU - HONG, Q. Y.
AU - KWONG, S.
PY - 2004
Y1 - 2004
N2 - This paper presents the design and implementation of text-independent speaker verification. We apply the maximum model distance (MMD) algorithm to the Gaussian mixture model (GMM) training. The traditional maximum likelihood (ML) method only utilizes the labeled utterances for each speaker model, which probably leads to a local optimization solution. By maximizing the model distance between the target and competing speakers, MMD could add the discriminative capability into the training procedure and then improve the verification performance. Based on the TIMIT corpus, we designed the verification experiments and the results show that the equal error rate (EER) could be reduced greatly compared with the traditional ML method. © 2004 IEEE.
AB - This paper presents the design and implementation of text-independent speaker verification. We apply the maximum model distance (MMD) algorithm to the Gaussian mixture model (GMM) training. The traditional maximum likelihood (ML) method only utilizes the labeled utterances for each speaker model, which probably leads to a local optimization solution. By maximizing the model distance between the target and competing speakers, MMD could add the discriminative capability into the training procedure and then improve the verification performance. Based on the TIMIT corpus, we designed the verification experiments and the results show that the equal error rate (EER) could be reduced greatly compared with the traditional ML method. © 2004 IEEE.
UR - http://www.scopus.com/inward/record.url?scp=20544449723&partnerID=8YFLogxK
U2 - 10.1109/IECON.2004.1431850
DO - 10.1109/IECON.2004.1431850
M3 - Conference paper (refereed)
SP - 1769
EP - 1774
BT - IECON Proceedings (Industrial Electronics Conference)
ER -