NDLI: Using maximum entropy (ME) model to incorporate gesture cues for SU detection

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Using maximum entropy (ME) model to incorporate gesture cues for SU detection

Content Provider	ACM Digital Library
Author	Chen, Lei Harper, Mary Huang, Zhongqiang
Abstract	Accurate identification of sentence units (SUs) in spontaneous speech has been found to improve the accuracy of speech recognition, as well as downstream applications such as parsing. In recent multimodal investigations, gestur]al features were utilized, in addition to lexical and prosodic cues from the speech channel, for detecting SUs in conversational interactions using a hidden Markov model (HMM) approach. Although this approach is computationally efficient and provides a convenient way to modularize the knowledge sources, it has two drawbacks for our SU task. First, standard HMM training methods maximize the joint probability of observations and hidden events, as opposed to the posterior probability of a hidden event given observations, a criterion more closely related to SU classification error. A second challenge for integrating gestural features is that their absence sanctions neither SU events nor non-events; it is only the co-timing of gestures with the speech channel that should impact our model. To address these problems, a Maximum Entropy (ME) model is used to combine multimodal cues for SU estimation. Experiments carried out on VACE multi-party meetings confirm that the ME modeling approach provides a solid framework for multimodal integration.
Starting Page	185
Ending Page	192
Page Count	8
File Format	PDF
ISBN	159593541X
DOI	10.1145/1180995.1181035
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2006-11-02
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Prosody Meetings Multimodal fusion Gesture Sentence boundary detection Language models
Content Type	Text
Resource Type	Article

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