Paper: Improving Automatic Speech Recognition for Lectures through Transformation-based Rules Learned from Minimal Data

ACL ID P09-1086
Title Improving Automatic Speech Recognition for Lectures through Transformation-based Rules Learned from Minimal Data
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2009
Authors

We demonstrate that transformation-based learning can be used to correct noisy speech recognition transcripts in the lec- ture domain with an average word error rate reduction of 12.9%. Our method is distinguished from earlier related work by its robustness to small amounts of training data, and its resulting efficiency, in spite of its use of true word error rate computations as a rule scoring function.