For speaker identification, a robust and effective feature extraction method is necessary. But in the current circumstance, there exists no perfect feature that could optimally characterize physiological difference among speakers regardless of personal variation. A soft competition scheme for optimal fusion of diverse feature sets is applied to speaker identification in order to achieve the improved performance. Based on a linear combination scheme, diverse feature vectors are used together while the winning feature vector through soft competition plays more important role in the representation. The simulations on KING corpus show that this alternative method could yield good performance for speaker identification.
Cite as: Wang, L., Chen, K., Chi, H. (2000) Optimal fusion of diverse feature sets for speaker identification: an alternative method. Proc. 6th International Conference on Spoken Language Processing (ICSLP 2000), vol. 2, 294-297, doi: 10.21437/ICSLP.2000-267
@inproceedings{wang00d_icslp,
author={Lan Wang and Ke Chen and Huisheng Chi},
title={{Optimal fusion of diverse feature sets for speaker identification: an alternative method}},
year=2000,
booktitle={Proc. 6th International Conference on Spoken Language Processing (ICSLP 2000)},
pages={vol. 2, 294-297},
doi={10.21437/ICSLP.2000-267}
}