|Department of Engineering|
|University of Cambridge > Engineering Department > Machine Intelligence Lab|
PERFORMANCE MEASURES FOR PHONE-LEVEL PRONUNCIATION TEACHING IN CALL
S. M. Witt and S. J. Young
This work presents a general development framework for automatic pronunciation assessment within computer-assisted language learning (CALL) together with several refinements of a previously described pronunciation scoring method. This method utilises a likelihood-based `Goodness of Pronunciation' (GOP) measure which in this work has been extended to include individual thresholds for each phone based on both averaged native confidence scores and on rejection statistics provided by human judges.These statistics where provided through a specifically recorded and annotated database of non-native speech. Since pronunciation assessment is highly subjective, a set of four performance measures has been designed, each of them measuring different aspects of how well computer-derived phone-level scores agree with human scores. These performance measures are used to cross-validate the reference annotations and to assess the basic GOP algorithm and its refinements. The experimental results suggest that a likelihood-based pronunciation scoring metric can achieve usable performance, especially after applying the various enhancements.
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