@inproceedings{f921011e1c0c46958f1c27f0d26876c4,
title = "Kernel multimodal discriminant analysis for speaker verification",
abstract = "In this paper, we propose a robust speaker feature extraction method using kernel multimodal Fisher discriminant analysis (kernel MFDA). Kernel MFDA has been designed to have the characteristics both of kernel principal component analysis (kernel PCA) and kernel Fisher discriminant analysis (kernel FDA). Therefore, the feature vectors extracted by kernel MFDA are denoised as well as discriminated. For evaluation, we compare our proposed method with principal component analysis (PCA) and kernel PCA on the speaker verification systems.",
keywords = "Feature extraction, Speaker recognition, Speech enhancement",
author = "Kim, {Min Seok} and Yang, {Il Ho} and Yu, {Ha Jin}",
year = "2010",
doi = "10.1109/ICASSP.2010.5495602",
language = "English",
isbn = "9781424442966",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4498--4501",
booktitle = "2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings",
address = "United States",
note = "2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 ; Conference date: 14-03-2010 Through 19-03-2010",
}