Context Dependent Feature Based Bottom-up Rescoring SVM Classifier in Children´s English Stress Mis-pronunciation Detection

Automatic assessment of word stress error is an integral part for oral language grading system. However, problems that the property of vowels depends on its context information and the data sparseness of different vowel class are yet to be solved. This paper shall briefly introduce a hybrid method consisting of both traditional prosodic features and proposed context dependent strategies. In classification word stress is determined by weighting a bottom-up fashioned group tree with modified distributed probability score. In experiment, the overall equal error rate of our proposed system achieves 9.41%, which exhibits relative reduction and its competence of use in stress error detection system.