Automatic lexical stress detection for Chinese learners´ of English

This paper investigates lexical stress detection for Chinese learners of English, where a combined differential acoustic feature is developed to represent the lexical stress of polysyllabic words in continuous speech. The use of frame-averaged feature and the contextual information intra-word can be input to the classifiers without normalization. The word-based stress detection method proposed in this paper employs the SVM to classify the stress patterns. In the experiments, a subset from TIMIT corpus is used with carefully selected target polysyllable words and sentences. Multiple SVMs are trained with the combined differential acoustic features. Both the speech from native and non-native speakers is tested for lexical stress detection. The detection system obtained an average word accuracy of 89.78% on speech from native speaker, and 77.37% on speech from Chinese learners.