Combining VTS model compensation and support vector machines

It is difficult to adapt discriminative classifiers, particularly kernel based ones such as support vector machines (SVMs), to handle mismatches between the training and test data. In previous work adaptation was performed by modifying the kernel used with the SVM, rather changing the SVM parameters themselves. However an idealised form of compensation, single pass retraining, was used to alter the generative models associated with the generative kernel. In this paper vector Taylor series model compensation is used. This scheme is more efficient and allows a noise model to be estimated. The performance of the new scheme is evaluated on two continuous digit tasks. On both tasks SVM-rescoring outperformed the baseline VTS compensated models.