1,252 research outputs found

    Models and Analysis of Vocal Emissions for Biomedical Applications

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    The International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA) came into being in 1999 from the particularly felt need of sharing know-how, objectives and results between areas that until then seemed quite distinct such as bioengineering, medicine and singing. MAVEBA deals with all aspects concerning the study of the human voice with applications ranging from the neonate to the adult and elderly. Over the years the initial issues have grown and spread also in other aspects of research such as occupational voice disorders, neurology, rehabilitation, image and video analysis. MAVEBA takes place every two years always in Firenze, Italy

    Continuous Analysis of Affect from Voice and Face

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    Human affective behavior is multimodal, continuous and complex. Despite major advances within the affective computing research field, modeling, analyzing, interpreting and responding to human affective behavior still remains a challenge for automated systems as affect and emotions are complex constructs, with fuzzy boundaries and with substantial individual differences in expression and experience [7]. Therefore, affective and behavioral computing researchers have recently invested increased effort in exploring how to best model, analyze and interpret the subtlety, complexity and continuity (represented along a continuum e.g., from −1 to +1) of affective behavior in terms of latent dimensions (e.g., arousal, power and valence) and appraisals, rather than in terms of a small number of discrete emotion categories (e.g., happiness and sadness). This chapter aims to (i) give a brief overview of the existing efforts and the major accomplishments in modeling and analysis of emotional expressions in dimensional and continuous space while focusing on open issues and new challenges in the field, and (ii) introduce a representative approach for multimodal continuous analysis of affect from voice and face, and provide experimental results using the audiovisual Sensitive Artificial Listener (SAL) Database of natural interactions. The chapter concludes by posing a number of questions that highlight the significant issues in the field, and by extracting potential answers to these questions from the relevant literature. The chapter is organized as follows. Section 10.2 describes theories of emotion, Sect. 10.3 provides details on the affect dimensions employed in the literature as well as how emotions are perceived from visual, audio and physiological modalities. Section 10.4 summarizes how current technology has been developed, in terms of data acquisition and annotation, and automatic analysis of affect in continuous space by bringing forth a number of issues that need to be taken into account when applying a dimensional approach to emotion recognition, namely, determining the duration of emotions for automatic analysis, modeling the intensity of emotions, determining the baseline, dealing with high inter-subject expression variation, defining optimal strategies for fusion of multiple cues and modalities, and identifying appropriate machine learning techniques and evaluation measures. Section 10.5 presents our representative system that fuses vocal and facial expression cues for dimensional and continuous prediction of emotions in valence and arousal space by employing the bidirectional Long Short-Term Memory neural networks (BLSTM-NN), and introduces an output-associative fusion framework that incorporates correlations between the emotion dimensions to further improve continuous affect prediction. Section 10.6 concludes the chapter

    Machine Learning Methods for Social Signal Processing

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    Emotional Design: An Overview

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    Emotional design has been well recognized in the domain of human factors and ergonomics. In this chapter, we reviewed related models and methods of emotional design. We are motivated to encourage emotional designers to take multiple perspectives when examining these models and methods. Then we proposed a systematic process for emotional design, including affective-cognitive needs elicitation, affective-cognitive needs analysis, and affective-cognitive needs fulfillment to support emotional design. Within each step, we provided an updated review of the representative methods to support and offer further guidance on emotional design. We hope researchers and industrial practitioners can take a systematic approach to consider each step in the framework with care. Finally, the speculations on the challenges and future directions can potentially help researchers across different fields to further advance emotional design.http://deepblue.lib.umich.edu/bitstream/2027.42/163319/1/Emotional_Design_Manuscript_Final.pdfSEL

    ACII 2009: Affective Computing and Intelligent Interaction. Proceedings of the Doctoral Consortium 2009

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