Название: Social Signal Processing Автор: Judee K. Burgoon, Nadia Magnenat-Thalmann, Maja Pantic
Издательство: Cambridge University Press
Год: 2017
Страниц: 440
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB
Social Signal Processing is the first book to cover all aspects of the modeling, automated detection, analysis, and synthesis of nonverbal behavior in human-human and human-machine interactions. Authoritative surveys address conceptual foundations, machine analysis and synthesis of social signal processing, and applications. Foundational topics include affect perception and interpersonal coordination in communication; later chapters cover technologies for automatic detection and understanding such as computational paralinguistics and facial expression analysis and for the generation of artificial social signals such as social robots and artificial agents. The final section covers a broad spectrum of applications based on social signal processing in healthcare, deception detection, and digital cities, including detection of developmental diseases and analysis of small groups. Each chapter offers a basic introduction to its topic, accessible to students and other newcomers, and then outlines challenges and future perspectives for the benefit of experienced researchers and practitioners in the field.
In this book we focus on systematization, analysis, and discussion of recent trends in machine learning methods for Social signal processing (SSP). Because social signaling is often of central importance to subconscious decision making that affects everyday tasks (e.g., decisions about risks and rewards, resource utilization, or interpersonal relationships), the need for automated understanding of social signals by computers is a task of paramount importance. Machine learning has played a prominent role in the advancement of SSP over the past decade. This is, in part, due to the exponential increase of data availability that served as a catalyst for the adoption of a new data-driven direction in affective computing. With the difficulty of exact modeling of latent and complex physical processes that underpin social signals, the data has long emerged as the means to circumvent or supplement expert- or physics-based models, such as the deformable musculoskeletal models of the human body, face, or hands and its movement, neuro-dynamical models of cognitive perception, or the models of the human vocal production. This trend parallels the role and success of machine learning in related areas, such as computer vision or audio, speech and language processing, that serve as the core tools for analytic SSP tasks.
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