Abstract

Discriminative classifiers are a popular approach to solving classification problems. However one of the problems with these approaches, in particular kernel based classifiers such as Support Vector Machines (SVMs), is that they are hard to adapt to mismatches between the training and test data. This paper describes a scheme for overcoming this problem for speech recognition in noise. Generative kernels, defined using generative models, allow SVMs to handle sequence data. By compensating the generative models for the noise conditions noise-specific generative kernels can be obtained. These can be used to train a noise-independent SVM on a range of noise conditions, which can then be used with a test-set noise kernel for classification. Initial experiments using an idealised version of model-based compensation were run on the AURORA 2.0 continuous digit task. The proposed scheme yielded large gains in performance over the compensated models.

Citations