To catch grammatical abnormalities possibly caused by speech recognition errors, two sets of syntactic features, linkage information and word associations based on syntactic dependency, are extracted for each word from the output of two lexicalized robust syntactic parsers respectively. Confidence measures, which combine features using Support Vector Machines, are used to detect speech recognition errors. A confidence measure that combines syntactic features with non-linguistic features yields consistent performance improvement in one or more aspects over those obtained by using non-linguistic features alone.

Semantic abnormalities possibly caused by speech recognition errors are caught by the analysis of semantic relatedness of a word to its context. Two different methods are used to integrate semantic analysis with syntactic analysis. One approach addresses the problem by extracting features for each word from its relations to other words. To this end, various WordNet-based measures and different context lengths are examined. The addition of semantic features in confidence measures can further yield small but consistent improvement in error detection performance. The other approach applies lexical cohesion analysis by taking both reiteration and collocation relationships into consideration and by augmenting words with probability predicted from syntactic analysis. Two WordNet-based measures and one measure based on Latent Semantic Analysis are used to instantiate lexical cohesion relationships. Additionally, various word probability thresholds and cosine similarity thresholds are examined. The incorporation of lexical cohesion analysis is superior to the use of syntactic analysis alone. In summary, the use of linguistic information as described, including syntactic and semantic information, can provide positive impact on automatic detection of speech recognition errors.]]> 360 1155691