Using SVMs and discriminative models for speech recognition

In speech recognition, standard MAP decoders attribute speech data to the class with the highest posterior probability. This minimises the error rate under assumptions of model correctness. This assumption is invalid for speech recognition with HMMs. Hence, an interesting question is whether extra, useful information about the speech source can be extracted from the HMMs and used to lower error rates in practical systems. In this paper additional features are extracted from HMMs and incorporated into a multidimensional score-space. SVMs are then used to implement a decision rule, Preliminary experiments are performed on a small speaker-independent isolated letter task. Score-spaces based on discriminative models are used with previous results based on generative models, Both score-spaces outperform standard schemes.