Classification of lexical stress using spectral and prosodic features for computer-assisted language learning systems
A system for classification of lexical stress for language learners is proposed. It successfully combines spectral and prosodic characteristics using GMMs. Models are learned on native speech, which does not require manual labeling. A method for controlling the operating point of the system is proposed. We achieve a 20% error rate on Japanese children speaking English.
prosodic features computer-assisted language learning lexical stress detection mel frequency cepstral coefficients gaussian mixture models