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2009

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

10 years 9 months ago
Improving Automatic Speech Recognition for Lectures through Transformation-based Rules Learned from Minimal Data
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.
Cosmin Munteanu, Gerald Penn, Xiaodan Zhu
Added 16 Feb 2011
Updated 16 Feb 2011
Type Journal
Year 2009
Where ACL
Authors Cosmin Munteanu, Gerald Penn, Xiaodan Zhu
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