Automatic syllable stress detection using prosodic features for pronunciation evaluation of language learners

A robust language learning system, designed to help students practice a foreign language along with a machine tutor, must provide meaningful feedback to users by isolating and localizing their pronunciation errors. This paper presents a new technique for automatic syllable stress detection that is tailored for language-learning purposes. Our method, which uses basic prosodic features and others related to the fundamental frequency slope and RMS energy range, is at least as accurate as an expert human listener, but requires no human supervision other than a pre-defined dictionary of expected lexical stress patterns for all words in the system's vocabulary. Optimal feature choices exhibited an 87-89% accuracy compared with human-tagged stress labels, exceeding the inter-human agreement commonly held to be about 80%.