Automatic pronunciation error detecting of Chinese using SVM with structural features

摘要

Pronunciation errors are often made by learners of a foreign language. To build a CALL (Computer-Aided Language Learning) system to support them, automatic error detection is an essential technique. In this study, Japanese learners of Chinese are focused on and automatic detection of their typical and frequent phoneme production errors is investigated. Due to difficulty of preparing labels of the phoneme errors found in real learners' data, we prepare native utterances including artificial phoneme errors. First, the target phonemes, which often appear to be problematic for Japanese learners to pronounce correctly, are defined through discussion with teachers. Then, phoneme production errors are created artificially by changing transcripts of native utterances and we further ask native speakers to read sentences with specific phoneme production errors. Three methods of GOP (Goodness of Pronunciation), LR (Likelihood Ratio), and SVM (Support Vector Machine) with structural features are compared under the task of phoneme error detection. Here, GOP-based error detection is done by using the threshold optimized through development data. Results show that SVM with structural features performs better than both of the GOP-based and LR-based baseline methods.