Sixth International Conference on Spoken Language Processing (ICSLP 2000)
October 16-20, 2000
Data-Dependent Kernels in Svm Classification of Speech Patterns
Nathan Smith (1), Mahesan Niranjan (2)
(1) Cambridge University Engineering Dept., UK
Support Vector Machines (SVMs) have recently proved to
be powerful pattern classification tools with a strong connection
to statistical learning theory. One of the hurdles to
using SVMs in speech recognition, and a crucial aspect of
SVM design in general, is the choice of the kernel function
for non-separable data, and the setting of its parameters.
This is often based on experience or a potentially costly
search. This paper gives some experimental justification
for the Fisher kernels proposed in ; kernels are obtained
and their extra regularisation and use of labelled and unlabelled
data discussed. Fisher kernels are derived from
generative probability models of the data, and are a first step
to implementing kernels for variable length sequences.
(2) Dept of Computer Science, Sheffield University, UK
- T. Jaakkola and D. Haussler. Exploiting generative
models in discriminative classifiers. In M.S. Kearns,
S.A. Solla, and D.A. Cohn, editors, Advances in Neural
Information Processing Systems 11. MIT Press, 1999.
Smith, Nathan / Niranjan, Mahesan (2000):
"Data-dependent kernels in svm classification of speech patterns",
In ICSLP-2000, vol.1, 297-300.