In this paper, we explore the model combination problem for rescoring Automatic Speech

Recognition (ASR) hypotheses. We use minimum Empirical Bayes Risk for the optimization

criterion and Deterministic Annealing techniques to search through the non-convex

parameter space. Our experiments on the DARPA WSJ task using several different language

models showed that our approach consistently outperforms the standard methods of model

combination that optimize using 1-best hypothesis error.

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Publisher  IEEE Spoken Language Technology Workshop

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