The unique research area of audio-visual speech recognition has attracted much interest in recent years as visual information about lip dynamics has been shown to improve the performance of automatic speech recognition systems, especially in noisy environments.""Visual Speech Recognition: Lip Segmentation and Mapping"" presents an up-to-date account of research done in the areas of lip segmentation, visual speech recognition, and speaker identification and verification. A useful reference for researchers working in this field, this book contains the latest research results from renowned experts with in-depth discussion on topics such as visual speaker authentication, lip modeling, and systematic evaluation of lip features.

This edited book will serve as a source of reference for technologies and applications for multimodality data analytics in big data environments. After an introduction, the editors organize the book into four main parts on sentiment, affect and emotion analytics for big multimodal data; unsupervised learning strategies for big multimodal data; supervised learning strategies for big multimodal data; and multimodal big data processing and applications. The book will be of value to researchers, professionals and students in engineering and computer science, particularly those engaged with image and speech processing, multimodal information processing, data science, and artificial intelligence.