SEARCH RESULTS BASED N-BEST HYPOTHESIS RESCORING WITH MAXIMUM ENTROPY CLASSIFICATION

We propose a simple yet effective method for improving speech recognition by reranking the N-best speech recog-nition hypotheses using search results. We model N-best reranking as a binary classification problem and select the hypothesis with the highest classification confidence. We use query-specific features extracted from the search results to en-code domain knowledge and use it with a maximum entropy classifier to rescore the N-best list. We show that rescoring even only the top 2 hypotheses, we can obtain a significant 3 % absolute sentence accuracy (SACC) improvement over a strong baseline on production traffic from an entertainment domain. Index Terms — N-best reranking, Voice search, Lan-guage modeling, Maximum entropy modeling 1