Neural Information Processing and VLSI provides a unified treatment of this important subject for use in classrooms, industry, and research laboratories, in order to develop advanced artificial and biologically-ins...
ISBN:
(纸本)9780792395478
Neural Information Processing and VLSI provides a unified treatment of this important subject for use in classrooms, industry, and research laboratories, in order to develop advanced artificial and biologically-inspired neural networks using compact analog and digital VLSI parallel processing techniques. Neural Information Processing and VLSI systematically presents various neural network paradigms, computing architectures, and the associated electronic/optical implementations using efficient VLSI design methodologies. Conventional digital machines cannot perform computationally-intensive tasks with satisfactory performance in such areas as intelligent perception, including visual and auditory signal processing, recognition, understanding, and logical reasoning (where the human being and even a small living animal can do a superb job). Recent research advances in artificial and biological neural networks have established an important foundation for high-performance information processing with more efficient use of computing resources. The secret lies in the design optimization at various levels of computing and communication of intelligent machines. Each neural network system consists of massively paralleled and distributed signal processors with every processor performing very simple operations, thus consuming little power. Large computational capabilities of these systems in the range of some hundred giga to several tera operations per second are derived from collectively parallel processing and efficient data routing, through well-structured interconnection networks. Deep-submicron very large-scale integration (VLSI) technologies can integrate tens of millions of transistors in a single silicon chip for complex signal processing and information manipulation. The book is suitable for those interested in efficient neurocomputing as well as those curious about neural network system applications. It has been especially prepared for use as a
The domain of speech processing has come to the point where researchers and engineers are concerned with how speech technology can be applied to new products, and how this technology will transform our future. One ...
ISBN:
(纸本)9780792396468
The domain of speech processing has come to the point where researchers and engineers are concerned with how speech technology can be applied to new products, and how this technology will transform our future. One important problem is to improve robustness of speech processing under adverse conditions, which is the subject of this book. Robust speech processing is a relatively new area which became a concern as technology started moving from laboratory to field applications. A method or an algorithm is robust if it can deal with a broad range of applications and adapt to unknown conditions. Robustness in Automatic Speech Recognition addresses all of the fundamental problems and issues in the area. The book is divided into three parts. The first provides the background necessary for understanding the rest of the material. It also emphasizes the problems of speech production and perception in noise along with popular techniques used in speech analysis and automatic speech recognition. Part Two discusses the problems relevant to robustness in automatic speech recognition and speech-based applications. It emphasizes intra- and inter-speaker variability as well as automatic speech recognition of Lombard, noisy and channel distorted speech. Finally, the third part covers recent advances in the field of robust automatic speech recognition. Audience: An invaluable reference. May be used as a text for advanced courses on the subject.
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