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ISCA International Workshop on Speech and Language Technology in Education (SLaTE 2011)Venice, Italy |
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ISCA International Workshop on Speech and Language Technology in Education (SLaTE 2011)Venice, Italy |
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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
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.