Word-Error Correction of Continuous Speech Recognition Based on Normalized Relevance Distance

In spite of the recent advancements being made in speech recognition, recognition errors are unavoidable in continuous speech recognition. In this paper, we focus on a word-error correction system for continuous speech recognition using confusion networks. Conventional N -gram correction is widely used; however, the performance degrades due to the fact that the N -gram approach cannot measure information between long distance words. In order to improve the performance of theN -gram model, we employ Normalized Relevance Distance (NRD) as a measure for semantic similarity between words. NRD can identify not only co-occurrence but also the correlation of importance of the terms in documents. Even if the words are located far from each other, NRD can estimate the semantic similarity between the words. The effectiveness of our method was evaluated in continuous speech recognition tasks for multiple test speakers. Experimental results show that our error-correction method is the most effective approach as compared to the methods using other features.