Automatic localization and diagnosis of pronunciation errors for second-language learners of English

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 feedbaconly 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.