A Temporal Analysis Of Emotion

The current trends in technology suggest that the next generation of services and devices allows smarter customization and automatic context recognition. Computers learn the behavior of the users and can offer them customized services depending on the context, location, and preferences. One of the most important challenges in human-machine interaction is the proper understanding of human emotions by machines and automated systems. In the recent years, the progress made in machine learning and pattern recognition led to the development of algorithms that are able to learn the detection and identification of human emotions from experience. These algorithms use different modalities such as image, speech, and physiological signals to analyze and learn human emotions. In many settings, the vocal information might be more available than other modalities due to widespread of voice sensors in phones, cars, and computer systems in general. In emotion analysis from speech, an audio utterance is represented by an ordered (in time) sequence of features or a multivariate time series. Typically, the sequence is further mapped into a global descriptor representative of the entire utterance/sequence. This descriptor is used for classification and analysis. In classic approaches, statistics are computed over the entire sequence and used as a global descriptor. This often results in the loss of temporal ordering from the original sequence. Emotion is a succession of acoustic events. By discarding the temporal ordering of these events in the mapping, the classic approaches cannot detect acoustic patterns that lead to a certain emotion. In this dissertation, we propose a novel feature mapping framework. The proposed framework maps temporally ordered sequence of acoustic features into data-driven global descriptors that integrate the temporal information from the original sequence. The framework contains three mapping algorithms. These algorithms integrate the temporal information implicitly and explicitly in the descriptor's representation. In the rst algorithm, the Temporal Averaging Algorithm, we average the data temporally using leaky integrators to produce a global descriptor that implicitly integrates the temporal information from the original sequence. In order to integrate the discrimination between classes in the mapping, we propose the Temporal Response Averaging Algorithm which combines the temporal averaging step of the previous algorithm and unsupervised learning to produce data driven temporal contextual descriptors. In the third algorithm, we use the topology preserving property of the Self-Organizing Maps and the continuous nature of speech to map a temporal sequence into an ordered trajectory representing the behavior over time of the input utterance on a 2-D map of emotions. The temporal information is integrated explicitly in the descriptor which makes it easier to monitor emotions in long speeches. The proposed mapping framework maps speech data of different length to the same equivalent representation which alleviates the problem of dealing with variable length temporal sequences. This is advantageous in real time setting where the size of the analysis window can be variable. Using the proposed feature mapping framework, we build a novel data-driven speech emotion detection and recognition system that indexes speech databases to facilitate the classification and retrieval of emotions. We test the proposed system using two datasets. The first corpus is acted. We showed that the proposed mapping framework outperforms the classic approaches while providing descriptors that are suitable for the analysis and visualization of humans' emotions in speech data. The second corpus is an authentic dataset. In this dissertation, we evaluate the performances of our system using a collection of debates. For that purpose, we propose a novel debate collection that is one of the first initiatives in the literature. We show that the proposed system is able to learn human emotions from debates.

"This volume is the result of a conference ... sponsored by the New York Academy of Sciences and held on June 23 through 25, 2000 in New York City"--Page [v].

Emotions are central to our practices and understanding of public life. This book examines the political, social and personal consequences of public emotions in relation to conflict, ritual, social classification, collective life, identity, memory and power and is a multidisciplinary collaboration showing the emotional character of public life.

Emotion Measurement, Second Edition highlights key elements of emotions that should be considered in the measurement of emotions in both academic and commercial environments. This edition begins with an updated review of basic studies of emotion, including the theory, physiology, and psychology of emotions, as these are the foundational studies which food scientists as well as product developers and marketing professionals need to be aware of. The second section highlights methods for studying emotions, and reviews the different approaches to emotion measurement: questionnaire self-report, behavioral, and physiological. This section explores the merits of intrinsic versus extrinsic measures of emotion. Some new measurement approaches have emerged since the first edition of this book. The book then presents practical applications, with chapters on emotion research in food and beverage, as well as in a range of products and clinical settings. The experience in testing product emotions has increased since the first edition when product emotion research was newer. Finally, Emotion Measurement, Second Edition provides coverage of cross-cultural research on emotions. This is critical because much of the newer commercial research is aimed at markets around the world, requiring methods that work in many cultures. And the universality of emotions has been a topic of research for decades. Taking both an academic and applied approach, Emotion Measurement, Second Edition will be an invaluable reference for those conducting basic academic research on emotions and for sensory and consumer scientists, and the product developers and marketing professionals they work alongside. Reviews both the academic and the applied strands of emotion measurement research Focuses on cross-cultural studies of emotions, which is currently lacking from most of the literature in the field Highlights methods for studying emotions in both basic and applied studies

Emotion regulation has traditionally been conceived as a deliberative process, but there is growing evidence that many emotion-regulation processes operate at implicit levels. Implicit emotion regulation is initiated automatically, without conscious intention, and aims at modifying the quality of emotional responding. This special issue showcases recent advances in theorizing and empirical research on implicit emotion regulation. Implicit emotion regulation is pervasive in everyday life and contributes considerably to the effectiveness of emotion regulation. The contributions to this special issue highlight the significance of implicit emotion regulation in psychological adaptation, goal-directed behavior, interpersonal behavior, personality functioning, and mental health.

This thoroughly revised new edition of a classic book provides a clinically inspired but scientifically guided approach to the biological foundations of human mental function in health and disease. It includes authoritative coverage of all the major areas related to behavioral neurology, neuropsychology, and neuropsychiatry. Each chapter, written by a world-renowned expert in the relevant area, provides an introductory background as well as an up-to-date review of the most recent developments. Clinical relevance is emphasized but is placed in the context of cognitive neuroscience, basic neuroscience, and functional imaging. Major cognitive domains such as frontal lobe function, attention and neglect, memory, language, prosody, complex visual processing, and object identification are reviewed in detail. A comprehensive chapter on behavioral neuroanatomy provides a background for brain-behavior interactions in the cerebral cortex, limbic system, basal ganglia, thalamus, and cerebullum. Chapters on temperolimbic epilepsy, major psychiatric syndromes, and dementia provide in-depth analyses of these neurobehavioral entities and their neurobiological coordinates. Changes for this second edition include the reflection throughout the book of the new and flourishing alliance of behavioral neurology, neuropsychology, and neuropsychiatry with cognitive science;major revision of all chapters; new authorship of those on language and memory; and the inclusion of entirely new chapters on psychiatric syndromes and the dementias. Both as a textbook and a reference work, the second edition of Principles of Behavioral and Cognitive Neurology represents an invaluable resource for behavioral neurologists, neuropsychologists, neuropsychiatrists, cognitive and basic neuroscientists, geriatricians, physiatrists, and their students and trainees.

Emotion: Theory, Research, and Experience, Volume 4: The Measurement of Emotion provides an examination of the key issue of how to measure emotion. The book contains articles that present different approaches to the study of emotional measurement. Contributors focus on such topics as mood measurement; cross-cultural examination of triggers of emotion; possible dimensions that underlie the language of affect; measurement of emotions in lower animals; and measuring emotions and their derivatives. Psychologists, psychiatrists, behavioral psychologists, teachers, and students will find the book a good reference book.