Anoop Deoras, Denis Filimonov, Mary Harper, and Fred Jelinek
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.
|Publisher||IEEE Spoken Language Technology Workshop|