Jianhua Tao, Tieniu Tan's Affective Information Processing PDF

By Jianhua Tao, Tieniu Tan

ISBN-10: 1848003056

ISBN-13: 9781848003057

ISBN-10: 1848003064

ISBN-13: 9781848003064

Affective info processing is meant to provide desktops the human-like functions of statement, interpretation and new release of have an effect on positive factors. it truly is crucial for traditional and powerful human-computer interplay and has develop into a truly sizzling study topic.

This cutting-edge quantity discusses the most recent advancements in affective details processing, and summarises the most important applied sciences researched, equivalent to facial features popularity, face animation, emotional speech synthesis, clever brokers, and digital truth. The targeted and obtainable assurance features a wide selection of subject matters, together with parts which glance to problem and enhance present learn. The publication is organised into the subsequent themed elements to help the readers of their figuring out of the sphere: Cognitive Emotion version, have an effect on in Speech, have an effect on in Face and have an effect on in Multimodal Interaction.

Topics and Features:

• stories at the state of the art study in affective details processing

• Summarises key applied sciences researched over the last few years, similar to affective speech processing, affective facial expressions, affective physique gesture and circulation, affective multi-modal platforms, etc.

• deals a foundational introductory bankruptcy that grounds readers in middle recommendations and principles

• Discusses purposes of this know-how, corresponding to in schooling opposed to bullying

• presents summarising conclusions throughout

• Describes have an effect on in multimodal information

• appears to be like at cognitive emotion modelling in typical language communication

• Explores emotion conception and popularity from speech, and in response to multimodal information

• contains face animation in response to a wide audio-visual database

• offers physiological sensing for affective computing

• Examines evolutionary expression of feelings in digital people utilizing lighting and pixels

Written to supply a chance for scientists, engineers and graduate scholars to discover the problems, suggestions and applied sciences during this interesting new sector, this ground-breaking publication offers a finished assessment and demanding perception into the sector and should turn out a necessary reference software and resource.

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Extra info for Affective Information Processing

Example text

These internal states were treated as unobservable variables in a Bayesian network model. Model dependencies were established according to experimentally demonstrated causal relations among these unobservable variables and observable quantities (expressions of emotion and personality) such as word choice, facial expression, speech, and so on. EM (Reilly, 1996) simulated the emotion decay over time for a specific set of emotions, according to the goal that generated them. A specific intensity threshold was defined for each emotion to be triggered.

In this case, models will have to include consideration of context variables that might bring the subject to over- or underestimate conditional likelihoods, and losses or gains due (respectively) to negative or positive events. That is, rather than assigning fixed parameters to the dynamic belief networks (as happens in emotional-mind), conditional probability distributions should be driven by selected context variables. 5). Acknowledgments This work was financed, in part, by HUMAINE, the European HumanMachine Interaction Network on Emotions (EC Contract 507422).

DBNs are an extension of BNs, to enable modeling temporal environments (Nicholson & Brady, 1994). A DBN is made up of interconnected time slices of usually structurally identical static BNs; previous time slices are used to predict or estimate the nodes’ state in the current time slice. The conditional probability distributions of nodes with parents in different time slices are defined by a state evolution model, which describes how the system evolves over time. Finally, DDNs (Russell & Norvig, 1995) extend DBNs to provide a mechanism for making rational decisions by combining probability and utility theory within changing environments: in addition to nodes representing discrete random variables over time, the networks contain utility and decision nodes.

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Affective Information Processing by Jianhua Tao, Tieniu Tan


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