Segmenting meetings into agenda items by extracting implicit supervision from human note-taking
Proceedings of the 12th international conference on Intelligent user interfaces
Error correction via a post-processor for continuous speech recognition
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 01
Learning to interpret utterances using dialogue history
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
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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.