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New Era for Robust Speech Recognition
This book covers the state-of-the-art in deep neural-network-based methods for noise robustness in distant speech recognition applications. It provides insights and detailed descriptions of some of the new concepts and key technologies in the field, including novel architectures for speech enhancement, microphone arrays, robust features, acoustic model adaptation, training data augmentation, and training criteria. The contributed chapters also include descriptions of real-world applications, benchmark tools and datasets widely used in the field. This book is intended for researchers and practitioners working in the field of speech processing and recognition who are interested in the latest deep learning techniques for noise robustness. It will also be of interest to graduate students in electrical engineering or computer science, who will find it a useful guide to this field of research.
The Lancaster Bar
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Foreign Service List
Includes field staffs of Foreign Service, U.S. missions to international organizations, Agency for International Development, ACTION, U.S. Information Agency, Peace Corps, Foreign Agricultural Service, and Department of Army, Navy and Air Force
Report of Operations
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Perceptual Inference in Generative Models
We see and hear so freely that to the casual observer it is not obvious that perception would be such a difficult problem for modern science to understand. David Marr suggested that an understanding of perception requires analyzing the problems it solves along with the assumptions necessary for a solution. In this thesis I maintain that generative probabilistic models are a powerful tool to implement Marr's approach. In generative models one has to explitly encode the assumptions and goals of perceptual problems, whereas specific knowledge of the world is gleaned from the sensory data by learning within the model. This thesis explores the use of generative models for understanding perception...
