Examining knowledge sources for human error correction

A variety of knowledge sources have been employed by error correction mechanisms to improve the usability of speech recognition (SR) technology. However, little is known about the effect of knowledge sources on human error correction. Advancing our understanding of the role of knowledge sources in human error correction could improve the state of automatic error correction. We selected three knowledge sources, including alternative list, imperfect context, and perfect context, and compared their usefulness to human error correction via an empirical user study. The results showed that knowledge sources had significant impact on the performance of human error correction. In particular, perfect context was the best that could significantly reduce word error rate without increasing the processing time.