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Communications of the ACM
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BEAT: the Behavior Expression Animation Toolkit
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Unsupervised learning for speech motion editing
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A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
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ACM Transactions on Graphics (TOG)
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Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Expressive Facial Animation Synthesis by Learning Speech Coarticulation and Expression Spaces
IEEE Transactions on Visualization and Computer Graphics
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ACM Transactions on Graphics (TOG)
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IVA '07 Proceedings of the 7th international conference on Intelligent Virtual Agents
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IVA '09 Proceedings of the 9th International Conference on Intelligent Virtual Agents
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IVA'06 Proceedings of the 6th international conference on Intelligent Virtual Agents
Nonverbal behavior generator for embodied conversational agents
IVA'06 Proceedings of the 6th international conference on Intelligent Virtual Agents
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MIG'11 Proceedings of the 4th international conference on Motion in Games
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IEEE Transactions on Audio, Speech, and Language Processing
Learning dynamic audio-visual mapping with input-output Hidden Markov models
IEEE Transactions on Multimedia
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IEEE Transactions on Multimedia
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Proceedings of the companion publication of the 19th international conference on Intelligent User Interfaces
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We demonstrate a method for generating a 3D virtual character performance from the audio signal by inferring the acoustic and semantic properties of the utterance. Through a prosodic analysis of the acoustic signal, we perform an analysis for stress and pitch, relate it to the spoken words and identify the agitation state. Our rule-based system performs a shallow analysis of the utterance text to determine its semantic, pragmatic and rhetorical content. Based on these analyses, the system generates facial expressions and behaviors including head movements, eye saccades, gestures, blinks and gazes. Our technique is able to synthesize the performance and generate novel gesture animations based on coarticulation with other closely scheduled animations. Because our method utilizes semantics in addition to prosody, we are able to generate virtual character performances that are more appropriate than methods that use only prosody. We perform a study that shows that our technique outperforms methods that use prosody alone.