2016
INTERSPEECH
INTERSPEECH 2016
Conversational Engagement Recognition Using Auditory and Visual Cues
Abstract
Automatic prediction of engagement in human-human and human-machine dyadic and multiparty interaction scenarios could greatly aid in evaluation of the success of communication. A corpus of eight face-to-face dyadic casual conversations was recorded and used as the basis for an engagement study, which examined the effectiveness of several methods of engagement level recognition. A convolutional neural network based analysis was seen to be the most effective.
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Conference Pioneer
β INTERSPEECH 2016
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Interdisciplinary Bridge
β Computer Vision and Machine Learning
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Keyword Pioneer
β conversational engagement
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Cross-Pollinator
β Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio