Concordance

Query N03-1001 18 >
Negative filter NN 13 >
Random sample 13 (606.1 per million)

tech,6-1-N03-1001,bq	This paper describes a method for <term>	utterance classification	</term> that does not require <term> manual	#2211 This paper describes a method forutterance classification that does not require manual transcription of training data.
other,12-1-N03-1001,bq	classification </term> that does not require <term>	manual transcription	</term> of <term> training data </term> . The	#2217 This paper describes a method for utterance classification that does not requiremanual transcription of training data.
lr,15-1-N03-1001,bq	<term> manual transcription </term> of <term>	training data	</term> . The method combines <term> domain	#2220 This paper describes a method for utterance classification that does not require manual transcription oftraining data.
model,3-2-N03-1001,bq	training data </term> . The method combines <term>	domain independent acoustic models	</term> with off-the-shelf <term> classifiers	#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.
tech,9-2-N03-1001,bq	acoustic models </term> with off-the-shelf <term>	classifiers	</term> to give <term> utterance classification	#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.
measure(ment),12-2-N03-1001,bq	off-the-shelf <term> classifiers </term> to give <term>	utterance classification performance	</term> that is surprisingly close to what	#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	can be achieved using conventional <term>	word-trigram recognition	</term> requiring <term> manual transcription	#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.
other,29-2-N03-1001,bq	word-trigram recognition </term> requiring <term>	manual transcription	</term> . In our method , <term> unsupervised	#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.
tech,4-3-N03-1001,bq	transcription </term> . In our method , <term>	unsupervised training	</term> is first used to train a <term> phone	#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.
model,12-3-N03-1001,bq	training </term> is first used to train a <term>	phone n-gram model	</term> for a particular <term> domain </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	<term> model </term> is then passed to a <term>	phone-string classifier	</term> . The <term> classification accuracy	#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.
measure(ment),1-4-N03-1001,bq	phone-string classifier </term> . The <term>	classification accuracy	</term> of the <term> method </term> is evaluated	#2291 Theclassification accuracy of the method is evaluated on three different spoken language system domains.
other,11-4-N03-1001,bq	</term> is evaluated on three different <term>	spoken language system domains	</term> . Motivated by the success of <term>	#2301 The classification accuracy of the method is evaluated on three differentspoken language system domains.


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