Speech Processing In Embedded Systems
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Speech Processing in Embedded Systems
Author: Priyabrata Sinha
language: en
Publisher: Springer Science & Business Media
Release Date: 2009-12-01
Speech Processing has rapidly emerged as one of the most widespread and well-understood application areas in the broader discipline of Digital Signal Processing. Besides the telecommunications applications that have hitherto been the largest users of speech processing algorithms, several non-traditional embedded processor applications are enhancing their functionality and user interfaces by utilizing various aspects of speech processing. "Speech Processing in Embedded Systems" describes several areas of speech processing, and the various algorithms and industry standards that address each of these areas. The topics covered include different types of Speech Compression, Echo Cancellation, Noise Suppression, Speech Recognition and Speech Synthesis. In addition this book explores various issues and considerations related to efficient implementation of these algorithms on real-time embedded systems, including the role played by processor CPU and peripheral functionality.
Speech Processing in Embedded Systems
Speech Processing has rapidly emerged as one of the most widespread and well-understood application areas in the broader discipline of Digital Signal Processing. Besides the telecommunications applications that have hitherto been the largest users of speech processing algorithms, several non-traditional embedded processor applications are enhancing their functionality and user interfaces by utilizing various aspects of speech processing. "Speech Processing in Embedded Systems" describes several areas of speech processing, and the various algorithms and industry standards that address each of these areas. The topics covered include different types of Speech Compression, Echo Cancellation, Noise Suppression, Speech Recognition and Speech Synthesis. In addition this book explores various issues and considerations related to efficient implementation of these algorithms on real-time embedded systems, including the role played by processor CPU and peripheral functionality.
Robust Speech Recognition in Embedded Systems and PC Applications
Author: Jean-Claude Junqua
language: en
Publisher: Springer Science & Business Media
Release Date: 2006-04-18
Robust Speech Recognition in Embedded Systems and PC Applications provides a link between the technology and the application worlds. As speech recognition technology is now good enough for a number of applications and the core technology is well established around hidden Markov models many of the differences between systems found in the field are related to implementation variants. We distinguish between embedded systems and PC-based applications. Embedded applications are usually cost sensitive and require very simple and optimized methods to be viable. Robust Speech Recognition in Embedded Systems and PC Applications reviews the problems of robust speech recognition, summarizes the current state of the art of robust speech recognition while providing some perspectives, and goes over the complementary technologies that are necessary to build an application, such as dialog and user interface technologies. Robust Speech Recognition in Embedded Systems and PC Applications is divided into five chapters. The first one reviews the main difficulties encountered in automatic speech recognition when the type of communication is unknown. The second chapter focuses on environment-independent/adaptive speech recognition approaches and on the mainstream methods applicable to noise robust speech recognition. The third chapter discusses several critical technologies that contribute to making an application usable. It also provides some design recommendations on how to design prompts, generate user feedback and develop speech user interfaces. The fourth chapter reviews several techniques that are particularly useful for embedded systems or to decrease computational complexity. It also presents some case studies for embedded applications and PC-based systems. Finally, the fifth chapter provides a future outlook for robust speech recognition, emphasizing the areas that the author sees as the most promising for the future. Robust Speech Recognition in Embedded Systems and PC Applications serves as a valuable reference and although not intended as a formal University textbook, contains some material that can be used for a course at the graduate or undergraduate level. It is a good complement for the book entitled Robustness in Automatic Speech Recognition: Fundamentals and Applications co-authored by the same author.