Infinite support vector machines in speech recognition
Jingzhou Yang, Rogier C. van Dalen, Mark Gales · 2013
Generative feature spaces provide an elegant way to apply dis-criminative models in speech recognition, and system perfor-mance has been improved by adapting this framework. How-ever, the classes in the feature space may be not linearly sepa-rable. Applying a linear classifier then limits performance. In-stead of a single classifier, this paper applies a mixture of ex-perts. This model trains different classifiers as experts focusing on different regions of the feature space. However, the num-ber 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 non-parametric approach. Index Terms: generative feature space, Bayesian non-parametric, Dirichlet process, mixture of experts, infinite sup-