DocumentCode :
2851891
Title :
Generalized linear discriminant sequence kernels for speaker recognition
Author :
Campbell, William M.
Author_Institution :
Motorola Human Interface Lab, Tempe, AZ 85284, USA
Volume :
1
fYear :
2002
fDate :
13-17 May 2002
Abstract :
Support Vector Machines have recently shown dramatic performance gains in many application areas. We show that the same gains can be realized in the area of speaker recognition via sequence kernels. A sequence kernel provides a numerical comparison of speech utterances as entire sequences rather than a probability at the frame level. We introduce a novel sequence kernel derived from generalized linear discriminants. The kernel has several advantages. First, the kernel uses an explicit expansion into “feature space”-this property allows all of the support vectors to be collapsed into a single vector creating a small speaker model. Second, the kernel retains the computational advantage of generalized linear discriminants trained using mean-squared error training. Finally, the kernel shows dramatic reductions in equal error rates over standard mean-squared error training in matched and mismatched conditions on a NIST speaker recognition task.
Keywords :
Computational modeling; Kernel; Marketing and sales; Mathematical model; Polynomials; Support vector machines;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech, and Signal Processing (ICASSP), 2002 IEEE International Conference on
Conference_Location :
Orlando, FL, USA
ISSN :
1520-6149
Print_ISBN :
0-7803-7402-9
Type :
conf
DOI :
10.1109/ICASSP.2002.5743679
Filename :
5743679
Link To Document :
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