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Inference algorithms for generative score-spaces

Using generative models, for example hidden Markov models (HMM), to derive features for a discriminative classifier has a number of advantages including the ability to make the features robust to speaker and noise changes. An interesting attribute of the derived features is that they may not have the same conditional independence assumptions as the underlying generative models, which are typically first-order Markovian. For efficiency these features are derived given a particular segmentation. This paper describes a general algorithm for obtaining the optimal segmentation with combined generative and discriminative models. Previous results, where the features were constrained to have first-order Markovian dependencies, are extended to allow derivative features to be used which are non-Markovian in nature. As an example, inference with zero and first-order HMM score-spaces is considered. Experimental results are presented on a noise-corrupted continuous digit string recognition task: AURORA 2.