Error-responsive feedback mechanisms for speech recognizers
Error-responsive feedback mechanisms for speech recognizers
Beyond n-grams: can linguistic sophistication improve language modeling?
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Combining knowledge sources to reorder N-best speech hypothesis lists
HLT '94 Proceedings of the workshop on Human Language Technology
A survey of types of text noise and techniques to handle noisy text
Proceedings of The Third Workshop on Analytics for Noisy Unstructured Text Data
Mining sequential patterns and tree patterns to detect erroneous sentences
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Error detection for statistical machine translation using linguistic features
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
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Recognition errors hinder the proliferation of speech recognition (SR) systems. Based on the observation that recognition errors may result in ungrammatical sentences, especially in dictation application where an acceptable level of accuracy of generated documents is indispensable, we propose to incorporate two kinds of linguistic features into error detection: lexical features of words, and syntactic features from a robust lexicalized parser. Transformation-based learning is chosen to predict recognition errors by integrating word confidence scores with linguistic features. The experimental results on a dictation data corpus show that linguistic features alone are not as useful as word confidence scores in detecting errors. However, linguistic features provide complementary information when combined with word confidence scores, which collectively reduce the classification error rate by 12.30% and improve the F measure by 53.62%.