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The outer lip and inner lip FAPs have been utilized in audio-visual speech recognition and satisfactory results have been achieved.
Multimodal signal processing is an important research and development field that processes signals and combines information from a variety of modalities – speech, vision, language, text – which significantly enhance the understanding, modelling, and performance of human-computer interaction devices or systems enhancing human-human communication. The overarching theme of this book is the application of signal processing and statistical machine learning techniques to problems arising in this multi-disciplinary field. It describes the capabilities and limitations of current technologies, and discusses the technical challenges that must be overcome to develop efficient and user-friendly multimodal interactive systems. With contributions from the leading experts in the field, the present book should serve as a reference in multimodal signal processing for signal processing researchers, graduate students, R&D engineers, and computer engineers who are interested in this emerging field. Presents state-of-art methods for multimodal signal processing, analysis, and modeling Contains numerous examples of systems with different modalities combined Describes advanced applications in multimodal Human-Computer Interaction (HCI) as well as in computer-based analysis and modelling of multimodal human-human communication scenes.
Provides several examples of applications using the MPEG-4 Facial Animation standard, including video and speech analysis. Covers the implementation of the standard on both the encoding and decoding side. Contributors includes individuals instrumental in the standardization process.
This book constitutes the thoroughly refereed post-conference proceedings of the COST Action 2102 and euCognition supported international school on Multimodal Signals: "Cognitive and Algorithmic Issues" held in Vietri sul Mare, Italy, in April 2008. The 34 revised full papers presented were carefully reviewed and selected from participants’ contributions and invited lectures given at the workshop. The volume is organized in two parts; the first on Interactive and Unsupervised Multimodal Systems contains 14 papers. The papers deal with the theoretical and computational issue of defining algorithms, programming languages, and determinist models to recognize and synthesize multimodal signals. These are facial and vocal expressions of emotions, tones of voice, gestures, eye contact, spatial arrangements, patterns of touch, expressive movements, writing patterns, and cultural differences, in anticipation of the implementation of intelligent avatars and interactive dialogue systems that could be exploited to improve user access to future telecommunication services. The second part of the volume, on Verbal and Nonverbal Communication Signals, presents 20 original studies devoted to the modeling of timing synchronisation between speech production, gestures, facial and head movements in human communicative expressions and on their mutual contribution for an effective communication.
This book constitutes the thoroughly refereed post-proceedings of the First International Workshop on Machine Learning for Multimodal Interaction, MLMI 2004, held in Martigny, Switzerland in June 2004. The 30 revised full papers presented were carefully selected during two rounds of reviewing and revision. The papers are organized in topical sections on HCI and applications, structuring and interaction, multimodal processing, speech processing, dialogue management, and vision and emotion.
"This book introduces the readers to the various aspects of visual speech recognitions, including lip segmentation from video sequence, lip feature extraction and modeling, feature fusion and classifier design for visual speech recognition and speaker verification" résumé de l'éditeur.
The need for natural and effective Human-Computer Interaction (HCI) is increasingly important due to the prevalence of computers in human activities. Computer vision and pattern recognition continue to play a dominant role in the HCI realm. However, computer vision methods often fail to become pervasive in the field due to the lack of real-time, robust algorithms, and novel and convincing applications. This state-of-the-art contributed volume is comprised of articles by prominent experts in computer vision, pattern recognition and HCI. It is the first published text to capture the latest research in this rapidly advancing field with exclusive focus on real-time algorithms and practical applications in diverse and numerous industries, and it outlines further challenges in these areas. Real-Time Vision for Human-Computer Interaction is an invaluable reference for HCI researchers in both academia and industry, and a useful supplement for advanced-level courses in HCI and Computer Vision.
A timely book containing foundations and current research directions on emotion recognition by facial expression, voice, gesture and biopotential signals This book provides a comprehensive examination of the research methodology of different modalities of emotion recognition. Key topics of discussion include facial expression, voice and biopotential signal-based emotion recognition. Special emphasis is given to feature selection, feature reduction, classifier design and multi-modal fusion to improve performance of emotion-classifiers. Written by several experts, the book includes several tools and techniques, including dynamic Bayesian networks, neural nets, hidden Markov model, rough sets, type-2 fuzzy sets, support vector machines and their applications in emotion recognition by different modalities. The book ends with a discussion on emotion recognition in automotive fields to determine stress and anger of the drivers, responsible for degradation of their performance and driving-ability. There is an increasing demand of emotion recognition in diverse fields, including psycho-therapy, bio-medicine and security in government, public and private agencies. The importance of emotion recognition has been given priority by industries including Hewlett Packard in the design and development of the next generation human-computer interface (HCI) systems. Emotion Recognition: A Pattern Analysis Approach would be of great interest to researchers, graduate students and practitioners, as the book Offers both foundations and advances on emotion recognition in a single volume Provides a thorough and insightful introduction to the subject by utilizing computational tools of diverse domains Inspires young researchers to prepare themselves for their own research Demonstrates direction of future research through new technologies, such as Microsoft Kinect, EEG systems etc.
This latest volume in the series, Socio-Affective Computing, presents a set of novel approaches to analyze opinionated videos and to extract sentiments and emotions. Textual sentiment analysis framework as discussed in this book contains a novel way of doing sentiment analysis by merging linguistics with machine learning. Fusing textual information with audio and visual cues is found to be extremely useful which improves text, audio and visual based unimodal sentiment analyzer. This volume covers the three main topics of: textual preprocessing and sentiment analysis methods; frameworks to process audio and visual data; and methods of textual, audio and visual features fusion. The inclusion of key visualization and case studies will enable readers to understand better these approaches. Aimed at the Natural Language Processing, Affective Computing and Artificial Intelligence audiences, this comprehensive volume will appeal to a wide readership and will help readers to understand key details on multimodal sentiment analysis.