Boosting of Prosodic and Pronunciation Features to Detect Mispronunciations of Non-Native Children

Commercial products that support L2-learners with computer assisted pronunciation training usually focus per exercise only on one possible pronunciation mistake that is typical for speakers of the respective L1 group. Acoustic models for words with wrong pronunciation are added to the system. In the present paper a more general approach with features that have proved to be widely independent of the learners' mother tongue is proposed. It is able to take various possible mistakes into consideration all at once. High dimensional feature vectors that encode prosodic varieties and differences of reference and recognized sentences are analyzed. With the ADABOOST algorithm those features are found, which contain the most important information to assess German children learning English. With 35 features 89 % of the agreement of experts is achieved.