Data-dependent kernels in svm classification of speech patterns
Support Vector Machines (SVMs) have recently proved to be powerful pattern classi cation 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 justi cation for the Fisher kernels proposed in [4]; 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 rststep to implementing kernels for variable length sequences.