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measure(ment),1-4-N03-1001,bq	The <term> classification accuracy </term> of the <term> method </term> is evaluated on three different <term> spoken language system domains </term> .	#2291 Theclassification accuracy of the method is evaluated on three different spoken language system domains.
tech,9-2-N03-1001,bq	The method combines <term> domain independent acoustic models </term> with off-the-shelf <term> classifiers </term> to give <term> utterance classification performance </term> that is surprisingly close to what can be achieved using conventional <term> word-trigram recognition </term> requiring <term> manual transcription </term> .	#2232 The method combines domain independent acoustic models with off-the-shelfclassifiers to give utterance classification performance that is surprisingly close to what can be achieved using conventional word-trigram recognition requiring manual transcription.
other,18-3-N03-1001,bq	In our method , <term> unsupervised training </term> is first used to train a <term> phone n-gram model </term> for a particular <term> domain </term> ; the <term> output </term> of <term> recognition </term> with this <term> model </term> is then passed to a <term> phone-string classifier </term> .	#2273 In our method, unsupervised training is first used to train a phone n-gram model for a particulardomain; the output of recognition with this model is then passed to a phone-string classifier.
model,3-2-N03-1001,bq	The method combines <term> domain independent acoustic models </term> with off-the-shelf <term> classifiers </term> to give <term> utterance classification performance </term> that is surprisingly close to what can be achieved using conventional <term> word-trigram recognition </term> requiring <term> manual transcription </term> .	#2226 The method combinesdomain independent acoustic models with off-the-shelf classifiers to give utterance classification performance that is surprisingly close to what can be achieved using conventional word-trigram recognition requiring manual transcription.
other,29-2-N03-1001,bq	The method combines <term> domain independent acoustic models </term> with off-the-shelf <term> classifiers </term> to give <term> utterance classification performance </term> that is surprisingly close to what can be achieved using conventional <term> word-trigram recognition </term> requiring <term> manual transcription </term> .	#2252 The method combines domain independent acoustic models with off-the-shelf classifiers to give utterance classification performance that is surprisingly close to what can be achieved using conventional word-trigram recognition requiringmanual transcription.
other,12-1-N03-1001,bq	This paper describes a method for <term> utterance classification </term> that does not require <term> manual transcription </term> of <term> training data </term> .	#2217 This paper describes a method for utterance classification that does not requiremanual transcription of training data.
tech,5-4-N03-1001,bq	The <term> classification accuracy </term> of the <term> method </term> is evaluated on three different <term> spoken language system domains </term> .	#2295 The classification accuracy of themethod is evaluated on three different spoken language system domains.
model,26-3-N03-1001,bq	In our method , <term> unsupervised training </term> is first used to train a <term> phone n-gram model </term> for a particular <term> domain </term> ; the <term> output </term> of <term> recognition </term> with this <term> model </term> is then passed to a <term> phone-string classifier </term> .	#2281 In our method, unsupervised training is first used to train a phone n-gram model for a particular domain; the output of recognition with thismodel is then passed to a phone-string classifier.
other,21-3-N03-1001,bq	In our method , <term> unsupervised training </term> is first used to train a <term> phone n-gram model </term> for a particular <term> domain </term> ; the <term> output </term> of <term> recognition </term> with this <term> model </term> is then passed to a <term> phone-string classifier </term> .	#2276 In our method, unsupervised training is first used to train a phone n-gram model for a particular domain; theoutput of recognition with this model is then passed to a phone-string classifier.
model,12-3-N03-1001,bq	In our method , <term> unsupervised training </term> is first used to train a <term> phone n-gram model </term> for a particular <term> domain </term> ; the <term> output </term> of <term> recognition </term> with this <term> model </term> is then passed to a <term> phone-string classifier </term> .	#2267 In our method, unsupervised training is first used to train aphone n-gram model for a particular domain; the output of recognition with this model is then passed to a phone-string classifier.
tech,32-3-N03-1001,bq	In our method , <term> unsupervised training </term> is first used to train a <term> phone n-gram model </term> for a particular <term> domain </term> ; the <term> output </term> of <term> recognition </term> with this <term> model </term> is then passed to a <term> phone-string classifier </term> .	#2287 In our method, unsupervised training is first used to train a phone n-gram model for a particular domain; the output of recognition with this model is then passed to aphone-string classifier.
tech,23-3-N03-1001,bq	In our method , <term> unsupervised training </term> is first used to train a <term> phone n-gram model </term> for a particular <term> domain </term> ; the <term> output </term> of <term> recognition </term> with this <term> model </term> is then passed to a <term> phone-string classifier </term> .	#2278 In our method, unsupervised training is first used to train a phone n-gram model for a particular domain; the output ofrecognition with this model is then passed to a phone-string classifier.
other,11-4-N03-1001,bq	The <term> classification accuracy </term> of the <term> method </term> is evaluated on three different <term> spoken language system domains </term> .	#2301 The classification accuracy of the method is evaluated on three differentspoken language system domains.
lr,15-1-N03-1001,bq	This paper describes a method for <term> utterance classification </term> that does not require <term> manual transcription </term> of <term> training data </term> .	#2220 This paper describes a method for utterance classification that does not require manual transcription oftraining data.
tech,4-3-N03-1001,bq	In our method , <term> unsupervised training </term> is first used to train a <term> phone n-gram model </term> for a particular <term> domain </term> ; the <term> output </term> of <term> recognition </term> with this <term> model </term> is then passed to a <term> phone-string classifier </term> .	#2259 In our method,unsupervised training is first used to train a phone n-gram model for a particular domain; the output of recognition with this model is then passed to a phone-string classifier.
tech,6-1-N03-1001,bq	This paper describes a method for <term> utterance classification </term> that does not require <term> manual transcription </term> of <term> training data </term> .	#2211 This paper describes a method forutterance classification that does not require manual transcription of training data.
measure(ment),12-2-N03-1001,bq	The method combines <term> domain independent acoustic models </term> with off-the-shelf <term> classifiers </term> to give <term> utterance classification performance </term> that is surprisingly close to what can be achieved using conventional <term> word-trigram recognition </term> requiring <term> manual transcription </term> .	#2235 The method combines domain independent acoustic models with off-the-shelf classifiers to giveutterance classification performance that is surprisingly close to what can be achieved using conventional word-trigram recognition requiring manual transcription.
tech,26-2-N03-1001,bq	The method combines <term> domain independent acoustic models </term> with off-the-shelf <term> classifiers </term> to give <term> utterance classification performance </term> that is surprisingly close to what can be achieved using conventional <term> word-trigram recognition </term> requiring <term> manual transcription </term> .	#2249 The method combines domain independent acoustic models with off-the-shelf classifiers to give utterance classification performance that is surprisingly close to what can be achieved using conventionalword-trigram recognition requiring manual transcription.


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