Generative feature spaces provide an elegant way to apply discriminative models in speech recognition, and system performance has been improved by adapting this framework. However, the classes in the feature space may be not linearly separable. Applying a linear classifier then limits performance. Instead of a single classifier, this paper applies a mixture of experts. This model trains different classifiers as experts focusing on different regions of the feature space. However, the number of experts is not known in advance. This problem can be bypassed by employing a Bayesian non-parametric model. In this paper, a specific mixture of experts based on the Dirichlet process, namely the infinite support vector machine, is studied. Experiments conducted on the noise-corrupted continuous digit task AURORA 2 show the advantages of this Bayesian nonparametric approach. Index Terms: generative feature space, Bayesian nonparametric, Dirichlet process, mixture of experts, infinite suppor
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