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In this article we take a step forward towards the application of Support Vector Machines (SVMs) to continuous speech recognition. As in previous work, we use SVMs to estimate emission probabilities in the context of an SVM/HMM system. However, training pairwise classifiers to discriminate between some of the HMM-states of very close phonetic classes produce unsatisfactory results. We propose a data-driven approach for selecting the HMM-states for which SVMs are trained and those ones that are implicitly tied.