An automatic system for detection of pronunciation errors by adult learners of English is embedded in a language-learning package. Four main features are: (1) a recognizer robust to non-native speech; (2) localization of phone- and word-level errors; (3) diagnosis of what sorts of phone-level errors took place; and (4) a lexical-stress detector. These tools together allow robust, consistent, and specific feedback on pronunciation errors, unlike many previous systems that provide feedback only at a more general level. The diagnosis technique searches for errors expected based on the student's mother tongue and uses a separate bias for each error in order to maintain a particular desired global false alarm rate. Results are presented here for non-native recognition on tasks of differing complexity and for diagnosis, based on a data set of artificial errors, showing that this method can detect many contrasts with a high hit rate and a low false alarm rate.
Atwell, E., Bisiani, R., Daneluzzi, F., Herron, D., Menzel, W., Morton, R., et al. (1999). Automatic Localization and Diagnosis of Pronunciation Errors For Second-Language Learners of English. In 6th European Conference on Speech Communication and Technology, EUROSPEECH 1999 (pp.855-858).
Automatic Localization and Diagnosis of Pronunciation Errors For Second-Language Learners of English
BISIANI, ROBERTO;
1999
Abstract
An automatic system for detection of pronunciation errors by adult learners of English is embedded in a language-learning package. Four main features are: (1) a recognizer robust to non-native speech; (2) localization of phone- and word-level errors; (3) diagnosis of what sorts of phone-level errors took place; and (4) a lexical-stress detector. These tools together allow robust, consistent, and specific feedback on pronunciation errors, unlike many previous systems that provide feedback only at a more general level. The diagnosis technique searches for errors expected based on the student's mother tongue and uses a separate bias for each error in order to maintain a particular desired global false alarm rate. Results are presented here for non-native recognition on tasks of differing complexity and for diagnosis, based on a data set of artificial errors, showing that this method can detect many contrasts with a high hit rate and a low false alarm rate.
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