Paper: Error Detection Using Linguistic Features

ACL ID H05-1006
Title Error Detection Using Linguistic Features
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2005

Recognition errors hinder the prolifera- tion of speech recognition (SR) systems. Based on the observation that recogni- tion errors may result in ungrammatical sentences, especially in dictation appli- cation where an acceptable level of ac- curacy of generated documents is indis- pensable, we propose to incorporate two kinds of linguistic features into error de- tection: lexical features of words, and syn- tactic features from a robust lexicalized parser. Transformation-based learning is chosen to predict recognition errors by in- tegrating word confidence scores with lin- guistic features. The experimental results on a dictation data corpus show that lin- guistic features alone are not as useful as word confidence scores in detecting er- rors.