| 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. |
| 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. |
| 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. |
| 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. |
| model,26-3-N03-1001,bq | of <term> recognition </term> with this <term> | model | </term> is then passed to a <term> phone-string | #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. |
| 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. |
| 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. |
| 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. |
| other,18-3-N03-1001,bq | n-gram model </term> for a particular <term> | domain | </term> ; the <term> output </term> of <term> recognition | #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. |
| other,21-3-N03-1001,bq | particular <term> domain </term> ; the <term> | output | </term> of <term> recognition </term> with this | #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. |
| 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,23-3-N03-1001,bq | domain </term> ; the <term> output </term> of <term> | recognition | </term> with this <term> model </term> is then | #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. |
| 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. |
| 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. |
| tech,5-4-N03-1001,bq | classification accuracy </term> of the <term> | method | </term> is evaluated on three different <term> | #2295 The classification accuracy of themethod is evaluated on three different spoken language system domains. |
| 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. |
| 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. |
