| tech,8-2-N03-1001,ak | independent acoustic models </term> with <term> | off-the-shelf classifiers | </term> to give <term> utterance classification | #2232 The method combines domain independent acoustic models withoff-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,18-3-N03-1001,ak | n-gram model </term> for a particular <term> | domain | </term> ; the <term> output </term> of <term> recognition | #2274 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,ak | particular <term> domain </term> ; the <term> | output | </term> of <term> recognition </term> with this | #2277 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. | |
| measure(ment),1-4-N03-1001,ak | phone-string classifier </term> . The <term> | classification accuracy | </term> of the method is evaluated on three | #2292 Theclassification accuracy of the method is evaluated on three different spoken language system domains. | |
| tech,32-3-N03-1001,ak | <term> model </term> is then passed to a <term> | phone-string classifier | </term> . The <term> classification accuracy | #2288 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. | |
| other,11-4-N03-1001,ak | method is evaluated on three different <term> | spoken language system domains | </term> . Motivated by the success of <term> | #2302 The classification accuracy of the method is evaluated on three differentspoken language system domains. | |
| model,26-3-N03-1001,ak | of <term> recognition </term> with this <term> | model | </term> is then passed to a <term> phone-string | #2282 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. | |
| tech,12-1-N03-1001,ak | classification </term> that does not require <term> | manual transcription | </term> of <term> training data </term> . The | #2218 This paper describes a method for utterance classification that does not requiremanual transcription of training data. | |
| tech,26-2-N03-1001,ak | can be achieved using conventional <term> | word-trigram recognition | </term> requiring <term> manual transcription | #2250 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. | |
| model,12-3-N03-1001,ak | training </term> is first used to train a <term> | phone n-gram model | </term> for a particular <term> domain </term> | #2268 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,3-2-N03-1001,ak | training data </term> . The method combines <term> | domain independent acoustic models | </term> with <term> off-the-shelf classifiers | #2227 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,29-2-N03-1001,ak | word-trigram recognition </term> requiring <term> | manual transcription | </term> . In our method , <term> unsupervised | #2253 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,ak | transcription </term> . In our method , <term> | unsupervised training | </term> is first used to train a <term> phone | #2260 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. | |
| lr,15-1-N03-1001,ak | <term> manual transcription </term> of <term> | training data | </term> . The method combines <term> domain | #2221 This paper describes a method for utterance classification that does not require manual transcription oftraining data. | |
| tech,6-1-N03-1001,ak | This paper describes a method for <term> | utterance classification | </term> that does not require <term> manual | #2212 This paper describes a method forutterance classification that does not require manual transcription of training data. | |
| tech,23-3-N03-1001,ak | domain </term> ; the <term> output </term> of <term> | recognition | </term> with this <term> model </term> is then | #2279 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. | |
| measure(ment),12-2-N03-1001,ak | off-the-shelf classifiers </term> to give <term> | utterance classification performance | </term> that is surprisingly close to what | #2236 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. |
