@techreport{TD:100479,
	att_abstract={{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 perception concerning lexical stress patterns by training stress patterns in a native speaker's utterances and make 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. }},
	att_authors={yk2984, mb3171},
	att_categories={C_IIS.11},
	att_copyright={{}},
	att_copyright_notice={{}},
	att_donotupload={true},
	att_private={false},
	att_projects={Natural_Voices},
	att_tags={automatic assessment, machine learning},
	att_techdoc={true},
	att_techdoc_key={TD:100479},
	att_url={},
	author={Yeon-jun Kim and Mark Beutnagel},
	institution={{SLaTE-2011 workshop (Speech and Language Technology in Education)}},
	month={August},
	title={{Automatic Assessment of American English Lexical Stress using Machine Learning Algorithms}},
	year=2011,
}