A maximum entropy approach to natural language processing
Computational Linguistics
Prosody-based automatic segmentation of speech into sentences and topics
Speech Communication - Special issue on accessing information in spoken audio
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
MacVisSTA: a system for multimodal analysis
Proceedings of the 6th international conference on Multimodal interfaces
A second-order Hidden Markov Model for part-of-speech tagging
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Non-verbal cues for discourse structure
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Structural event detection for rich transcription of speech
Structural event detection for rich transcription of speech
Melodic cues to turn-taking in English: evidence from perception
SIGDIAL '01 Proceedings of the Second SIGdial Workshop on Discourse and Dialogue - Volume 16
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
VACE multimodal meeting corpus
MLMI'05 Proceedings of the Second international conference on Machine Learning for Multimodal Interaction
A multimodal analysis of floor control in meetings
MLMI'06 Proceedings of the Third international conference on Machine Learning for Multimodal Interaction
Using group history to identify character-directed utterances in multi-child interactions
SIGDIAL '12 Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Gaze and turn-taking behavior in casual conversational interactions
ACM Transactions on Interactive Intelligent Systems (TiiS) - Special issue on interaction with smart objects, Special section on eye gaze and conversation
Predicting next speaker and timing from gaze transition patterns in multi-party meetings
Proceedings of the 15th ACM on International conference on multimodal interaction
Proceedings of the 15th ACM on International conference on multimodal interaction
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Floor control is a scheme used by people to organize speaking turns in multi-party conversations. Identifying the floor control shifts is important for understanding a conversation's structure and would be helpful for more natural human computer interaction systems. Although people tend to use verbal and nonverbal cues for managing floor control shifts, only audio cues, e.g., lexical and prosodic cues, have been used in most previous investigations on speaking turn prediction. In this paper, we present a statistical model to automatically detect floor control shifts using both verbal and nonverbal cues. Our experimental results show that using a combination of verbal and nonverbal cues provides more accurate detection.