Applying example-based error correction selectively

This paper presents a supervised approach to combining detection and correction of speech recognition errors. For each word in a recognition result, our example-based correction algorithm generates a correction candidate by aligning the recognition result and an example sentence in the corpus. The distance between the aligned sentences is regarded as the reliability of the candidate. Then, an SVM (support vector machine) classifier judges whether the correction candidate should chosen by referring to the reliability score of the candidate and multiple confidence measures that are obtained from the recognition result. Experiments carried out on a travel task corpus have shown that the proposed approach achieved a 20 % reduction (from 10 % to 8 % absolute) in WER.