ISCA International Workshop on Speech and Language Technology in Education (SLaTE 2011)

Venice, Italy
August 24-26, 2011

Automatic Assessment of American English Lexical Stress using Machine Learning Algorithms

Yeon-Jun Kim, Mark C. Beutnagel

AT&T Labs – Research, Florham Park, NJ, USA

This paper introduces a method to assess lexical stress patterns in American English words automatically using machine learning algorithms, which could be used on the computer assisted language learning (CALL) system. We aim to model human production concerning lexical stress patterns by training stress patterns in a native speaker’s utterances and making use of it to detect erroneous stress patterns from a trainee. In this paper, all the possible lexical stress patterns in 3- and 4-syllable American English words are presented and four machine learning algorithms, CART, AdaBoost+CART, SVM and MaxEnt, are trained with acoustic measurements from a native speaker’s utterances and corresponding stress patterns. Our experimental results show that MaxEnt correctly classified the best, 83.3% stress patterns of 3-syllable words and 88.7% of 4-syllable words.
Index Terms. automatic assessment, lexical stress, machine learning

Full Paper

Bibliographic reference.  Kim, Yeon-Jun / Beutnagel, Mark C. (2011): "Automatic assessment of american English lexical stress using machine learning algorithms", In SLaTE-2011, 93-96.