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This paper proposes a novel technique to exploit generative and discriminative models for speech recognition. Speech recognition using discriminative models has attracted much attention in the past decade. In particular, a rescoring framework using discriminative word classifiers with generative-model-based features was shown to be effective in small-vocabulary tasks. However, a straightforward application of the framework to large-vocabulary tasks is difficult because the number of classifiers increases in proportion to the number of word pairs. We extend this framework to exploit generative and discriminative models in large-vocabulary tasks. N-best hypotheses obtained in the first pass are rescored using AdaBoost phoneme classifiers, where generative-model-based features, i.e. difference-of-likelihood features in particular, are used for the classifiers. Special care is taken to use context-dependent hidden Markov models (CDHMMs) as generative models, since most of the state-of-the-art speech recognizers use CDHMMs. Experimental results show that the proposed method reduces word errors by 32.68% relatively in a one-million-vocabulary isolated word recognition task.