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