TY - GEN
T1 - Discriminative training for speaker identification based on maximum model distance algorithm
AU - HONG, Q. Y.
AU - KWONG, S.
PY - 2004
Y1 - 2004
N2 - In this paper we apply the Maximum model distance (MMD) training to speaker identification and a new selection strategy of competitive speakers is proposed to it. The traditional ML method only utilizes the utterances for each speaker model, which probably leads to a local optimization solution. By maximizing the dissimilarities among those similar speaker models, MMD could add the discriminative capability into the training procedure and then improve the identification performance. Based on the TIMIT corpus, we designed the word and sentence experiments to evaluate this proposed training approach. The results show that the identification performance can be improved greatly when the training data is limited.
AB - In this paper we apply the Maximum model distance (MMD) training to speaker identification and a new selection strategy of competitive speakers is proposed to it. The traditional ML method only utilizes the utterances for each speaker model, which probably leads to a local optimization solution. By maximizing the dissimilarities among those similar speaker models, MMD could add the discriminative capability into the training procedure and then improve the identification performance. Based on the TIMIT corpus, we designed the word and sentence experiments to evaluate this proposed training approach. The results show that the identification performance can be improved greatly when the training data is limited.
UR - http://www.scopus.com/inward/record.url?scp=4544369753&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2004.1325913
DO - 10.1109/ICASSP.2004.1325913
M3 - Conference paper (refereed)
AN - SCOPUS:4544369753
SN - 0780384849
VL - 1
T3 - Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing
SP - I25-I28
BT - Proceedings of the 2004 IEEE International Conference on Acoustics, Speech and Signal Processing
PB - IEEE
T2 - 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing
Y2 - 17 May 2004 through 21 May 2004
ER -