Beyond emotion archetypes: Databases for emotion modelling using neural networks

Cowie, Roddy; Douglas‐Cowie, Ellen; Cox, Cate

doi:10.1016/j.neunet.2005.03.002

Cited by 104 publications

(66 citation statements)

References 38 publications

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“…These databases comprise a wide range of mocap data from diverse categories including locomotion, sport activities, and everyday life motions. Even though these databases are useful for analyzing the social and relational behaviors of virtual agents, they lack of expressive data carrying information about the meaning conveyed by body movements [6] and the expressive content.…”

Section: Related Workmentioning

confidence: 99%

Corpus Creation and Perceptual Evaluation of Expressive Theatrical Gestures

Carreno‐Medrano

Gibet

Larboulette

et al. 2014

Intelligent Virtual Agents

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Abstract. While human communication involves rich, complex and expressive gestures, available corpora of captured motions used for the animation of virtual characters contain actions ranging from locomotion to everyday life motions. We aim at creating a novel corpus of expressive and meaningful gestures, and we focus on body movements and gestures involved in theatrical scenarios. In this paper we propose a methodology for building a corpus of full-body theatrical gestures based on a magician show enriched with affective content. We then validate the constructed corpus of theatrical gestures and sequences through several perceptual studies focusing on the complexity of the produced movements as well as the recognizability of the additional affective content.

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Section: Related Workmentioning

confidence: 99%

Corpus Creation and Perceptual Evaluation of Expressive Theatrical Gestures

Carreno‐Medrano

Gibet

Larboulette

et al. 2014

Intelligent Virtual Agents

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“…A review of Devillers et al (2005); Cowie et al (2005); Ververidis and Kotropoulos (2006) provides a deep insight into databases and feature extraction methods covering recent advances in speech and emotion research. These tutorials review previous studies such as those of Murray and Arnott (1993); Cowie et al (2001); Scherer (2003); Douglas-Cowie et al (2003); Schröder (2004).…”

Section: Preprint Submitted To Speech Communicationmentioning

confidence: 99%

Automatic refinement of an expressive speech corpus assembling subjective perception and automatic classification

Iriondo

Planet

Socoró

et al. 2009

Speech Communication

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tomatic refinement of an expressive speech corpus assembling subjective perception and automatic classification. Speech Communication, Elsevier : North-Holland, 2009, 51 (9) This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. ACCEPTED MANUSCRIPT AbstractThis paper presents an automatic system able to enhance expressiveness in speech corpora recorded from acted or stimulated speech. The system is trained with the results of a subjective evaluation carried out on a reduced set of the original corpus. Once the system has been trained, it is able to check the complete corpus and perform an automatic pruning of the unclear utterances, i.e. with expressive styles which are different from the intended corpus. The content which most closely matches the subjective classification remains in the resulting corpus. An expressive speech corpus in Spanish, designed and recorded for speech synthesis purposes, has been used to test the presented proposal. The automatic refinement has been applied to the whole corpus and the result has been validated with a second subjective test.

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“…acquiring a corpus of acted speech, then annotating sequences containing affect within the corpus. In the case of automatic recognition of episodic emotions, this approach is plausible, based on the assumption that clear-cut bursts of episodic emotion will look and sound somewhat similar in most contexts [28]. However, recognition of pervasive emotions present a much greater challenge and, intuitively, one would think that awareness of personal and contextual information needs to be integrated into the recognition process.…”

Section: Discussion On the Elicitation Methodsmentioning

confidence: 99%

“…One inherent weakness in this premise, for emotion recognition, is in the elicitation method of the sample data. The topic of the elicitation of emotional speech samples has been well covered by other reviews [26,27,28], so it is only briefly covered in the next section.…”

Section: Recognition Of Emotions By Machinesmentioning

confidence: 99%

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The Composite Sensing of Affect

McIntyre

Göcke

Affect and Emotion in Human-Computer Interaction

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Abstract. This paper describes some of the issues faced by typical emotion recognition systems and the need to be able to deal with emotions in a natural setting. Studies tend to ignore the dynamic, versatile and personalised nature of affective expression and the influence that social setting, context and culture have on its rules of display. Affective cues can be present in multiple modalities and they can manifest themselves in different temporal order. Thus, fusing the feature sets is challenging. We present a composite approach to affective sensing. The term composite is used to reflect the blending of information from multiple modalities with the available semantic evidence to enhance the emotion recognition process.

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Beyond emotion archetypes: Databases for emotion modelling using neural networks

Cited by 104 publications

References 38 publications

Corpus Creation and Perceptual Evaluation of Expressive Theatrical Gestures

Corpus Creation and Perceptual Evaluation of Expressive Theatrical Gestures

Automatic refinement of an expressive speech corpus assembling subjective perception and automatic classification

The Composite Sensing of Affect

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