Support vector machines for segmental minimum Bayes risk decoding of continuous speech

Segmental minimum Bayes risk (SMBR) decoding involves the refinement of the search space into sequences of small sets of confusable words. We describe the application of support vector machines (SVMs) as discriminative models for the refined search spaces. We show that SVMs, which in their basic formulation are binary classifiers of fixed dimensional observations, can be used for continuous speech recognition. We also study the use of GiniSVMs, which is a variant of the basic SVM. On a small vocabulary task, we show this two pass scheme outperforms MMI (maximum mutual information) trained HMMs. Using system combination we also obtain further improvements over discriminatively trained HMMs.