C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
Experiments in spoken document retrieval
Information Processing and Management: an International Journal - Special issue on history of information science
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Speech Communication - Special issue on interactive voice technology for telecommunication applications (IVITA '96)
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
A Brief Introduction to Boosting
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Variant transduction: a method for rapid development of interactive spoken interfaces
SIGDIAL '01 Proceedings of the Second SIGdial Workshop on Discourse and Dialogue - Volume 16
Active learning for classifying phone sequences from unsupervised phonotactic models
NAACL-Short '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume of the Proceedings of HLT-NAACL 2003--short papers - Volume 2
A phonotactic-semantic paradigm for automatic spoken document classification
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Extending boosting for large scale spoken language understanding
Machine Learning
Extending boosting for large scale spoken language understanding
Machine Learning
Automatic discovery of topics and acoustic morphemes from speech
Computer Speech and Language
Detection of dialogue acts using perplexity-based word clustering
TSD'07 Proceedings of the 10th international conference on Text, speech and dialogue
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This paper describes a method for utterance classification that does not require manual transcription of training data. 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 requiring manual transcription. 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. The classification accuracy of the method is evaluated on three different spoken language system domains.