Statistical methods for speech recognition
Statistical methods for speech recognition
``Pause Units'' and Analysis of Spontaneous Japanese Dialogues: Preliminary Studies
ECAI '96 Workshop on Dialogue Processing in Spoken Language Systems
Utterance Units in Spoken Dialogue
ECAI '96 Workshop on Dialogue Processing in Spoken Language Systems
Social correlates of turn-taking behavior
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Modeling vocal interaction for segmentation in meeting recognition
MLMI'07 Proceedings of the 4th international conference on Machine learning for multimodal interaction
Optimizing the turn-taking behavior of task-oriented spoken dialog systems
ACM Transactions on Speech and Language Processing (TSLP)
| Hi-index | 0.00 |
Substantial research effort has been invested in recent decades into the computational study and automatic processing of multi-party conversation. While most aspects of conversational speech have benefited from a wide availability of analytic, computationally tractable techniques, only qualitative assessments are available for characterizing multi-party turn-taking. The current paper attempts to address this deficiency by first proposing a framework for computing turn-taking model perplexity, and then by evaluating several multi-participant modeling approaches. Experiments show that direct multi-participant models do not generalize to held out data, and likely never will, for practical reasons. In contrast, the Extended-Degree-of-Overlap model represents a suitable candidate for future work in this area, and is shown to successfully predict the distribution of speech in time and across participants in previously unseen conversations.