Automatic scoring of non-native spontaneous speech in tests of spoken English

This paper presents the first version of the SpeechRater system for automatically scoring non-native spontaneous high-entropy speech in the context of an online practice test for prospective takers of the Test of English as a Foreign Language internet-based test (TOEFL iBT). The system consists of a speech recognizer trained on non-native English speech data, a feature computation module, using speech recognizer output to compute a set of mostly fluency based features, and a multiple regression scoring model which predicts a speaking proficiency score for every test item response, using a sub-set of the features generated by the previous component. Experiments with classification and regression trees (CART) complement those performed with multiple regression. We evaluate the