Learning N-Best Correction Models from Implicit User Feedback in a Multi-Modal Local Search Application

Abstract

We describe a novel n-best correction model that can leverage implicit user feedback (in the form of clicks) to improve performance in a multi-modal speech-search application. The proposed model works in two stages. First, the n-best list generated by the speech recognizer is expanded with additional candidates, based on confusability information captured via user click statistics. In the second stage, this expanded list is rescored and pruned to produce a more accurate and compact n-best list. Results indicate that the proposed n-best correction model leads to significant improvements over the existing baseline, as well as other traditional n-best rescoring approaches.