Class-based n-gram models of natural language
Computational Linguistics
A maximum entropy approach to natural language processing
Computational Linguistics
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Prosody-based automatic segmentation of speech into sentences and topics
Speech Communication - Special issue on accessing information in spoken audio
TnT: a statistical part-of-speech tagger
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
Structural event detection for rich transcription of speech
Structural event detection for rich transcription of speech
Parsing conversational speech using enhanced segmentation
HLT-NAACL-Short '04 Proceedings of HLT-NAACL 2004: Short Papers
Using prosody for automatic sentence segmentation of multi-party meetings
TSD'06 Proceedings of the 9th international conference on Text, Speech and Dialogue
Recent innovations in speech-to-text transcription at SRI-ICSI-UW
IEEE Transactions on Audio, Speech, and Language Processing
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This paper compares language modeling techniques for dialog act segmentation of multiparty meetings. The evaluation is twofold; we search for a convenient representation of textual information and an efficient modeling approach. The textual features capture word identities, parts-of-speech, and automatically induced classes. The models under examination include hidden event language models, maximum entropy, and BoosTexter. All presented methods are tested using both human-generated reference transcripts and automatic transcripts obtained from a state-of-the-art speech recognizer.