In this paper, we focus on the problems associated with error correction of automatic speech recognition (ASR) based on confusion networks. The problems discussed are the availability of corpus in terms of calculating the semantic score and performance degradation for error correction using N-gram due to the null transitions in the confusion networks. In attempt to solve these problems, first, we employ Normalized Web Distance as a measure for semantic similarity between words that are located far from each other. The advantage of Normalized Web Distance is that it may use the Internet and so on for learning semantic similarity, which might solve the problem of corpus availability. Secondly, an error correction model without null nodes in confusion networks is trained using conditional random fields in order to improve the performance of error correction using N-grams.
Bibliographic reference. Byambakhishig, E. / Tanaka, K. / Aihara, Ryo / Nakashika, Toru / Takiguchi, Tetsuya / Ariki, Yasuo (2014): "Error correction of automatic speech recognition based on normalized web distance", In INTERSPEECH-2014, 2852-2856.