Abstract
We demonstrate that transformation-based learning can be used to correct noisy speech recognition transcripts in the lecture 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.
Cite
CITATION STYLE
Munteanu, C., Penn, G., & Zhu, X. (2009). Improving Automatic Speech Recognition for Lectures through Transformation-based Rules Learned from Minimal Data. In ACL-IJCNLP 2009 - Joint Conf. of the 47th Annual Meeting of the Association for Computational Linguistics and 4th Int. Joint Conf. on Natural Language Processing of the AFNLP, Proceedings of the Conf. (pp. 764–772). Association for Computational Linguistics (ACL). https://doi.org/10.3115/1690219.1690253
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