Two-step correction of speech recognition errors based on n-gram and long contextual information
Nakatani Ryohei, Tetsuya Takiguchi, Yasuo Ariki · 2013
This paper presents a fully automatic word error correction on a confusion network that makes use of long contextual information. However, a problem with long contextual information is that improvement of the recognition accuracy is minimal because of the word errors surrounding words. In this paper, recognition errors are first reduced by error correction using Ngram features. After that, the long-distance context scores are applied to the correction of the residual recognition errors. Index Terms: confusion network, conditional random fields, word-error correction, long contextual information