Lexical modeling of non-native speech for automatic speech recognition
نویسندگان
چکیده
This paper examines the recognition of non-native speech in jupiter, a speaker-independent, spontaneous-speech conversational system. Because the non-native speech in this domain is limited and varied, speakerand accent-specific methods are impractical. We therefore chose to model all of the non-native data with a single model. In particular, this paper describes an attempt to better model non-native lexical patterns. These patterns are incorporated by applying context-independent phonetic confusion rules, whose probabilities are estimated from training data. Using this approach, the word error rate on a non-native test set is reduced from 20.9% to 18.8%.
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