Recent advances in robust speech recognition technology / editors, Javier Ramirez, Juan Manuel Górriz.

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Material type: Text

TextPublication details: [Place of publication not identified] : Bentham Science Publishers, [2011]Description: 1 online resource (vi, 210 pages) : illustrationsContent type:

text

Media type:

computer

Carrier type:

online resource

ISBN:

9781608051724
1608051722

Subject(s):

Genre/Form:

Additional physical formats: Print version:: Recent Advances in Robust Speech Recognition Technology.DDC classification:

006.4/54

LOC classification:

TK7882.S65 R43 2011eb

Online resources:

Click here to access online

Contents:

02 Front_Back.pdf; 03 eBooks End User License Agreement-Website; 04 Content; 05 Foreword; 06 Preface; 07 Contributors; 08 Chapter1; 09 Chapter2; 09 Chapter2; 10 Chapter3; 11 chapter4; 12 Chapter5; 12 Chapter5; 13 Chapter6; 14 Chapter7; 15 Chapter8; 16 Chapter9; 17 Chapter10; 18 Chapter11; 19 Chapter12; 20 Chapter13; 21 Chapter14; 21 Chapter14; 22 index.

Summary: Annotation This E-book is a collection of articles that describe advances in speech recognition technology. Robustness in speech recognition refers to the need to maintain high speech recognition accuracy even when the quality of the input speech is degraded, or when the acoustical, articulate, or phonetic characteristics of speech in the training and testing environments differ. Obstacles to robust recognition include acoustical degradations produced by additive noise, the effects of linear filtering, nonlinearities in transduction or transmission, as well as impulsive interfering sources, and diminished accuracy caused by changes in articulation produced by the presence of high-intensity noise sources. Although progress over the past decade has been impressive, there are significant obstacles to overcome before speech recognition systems can reach their full potential. Automatic speech recognition (ASR) systems must be robust to all levels, so that they can handle background or channel noise, the occurrence on unfamiliar words, new accents, new users, or unanticipated inputs. They must exhibit more 'intelligence' and integrate speech with other modalities, deriving the user's intent by combining speech with facial expressions, eye movements, gestures, and other input features, and communicating back to the user through multimedia responses. Therefore, as speech recognition technology is transferred from the laboratory to the marketplace, robustness in recognition becomes increasingly significant. This E-book should be useful to computer engineers interested in recent developments in speech recognition technology.

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Item type	Home library	Collection	Call number	Materials specified	Status	Date due	Barcode
Electronic-Books	OPJGU Sonepat- Campus	E-Books EBSCO			Available

Includes bibliographical references and index.

Annotation This E-book is a collection of articles that describe advances in speech recognition technology. Robustness in speech recognition refers to the need to maintain high speech recognition accuracy even when the quality of the input speech is degraded, or when the acoustical, articulate, or phonetic characteristics of speech in the training and testing environments differ. Obstacles to robust recognition include acoustical degradations produced by additive noise, the effects of linear filtering, nonlinearities in transduction or transmission, as well as impulsive interfering sources, and diminished accuracy caused by changes in articulation produced by the presence of high-intensity noise sources. Although progress over the past decade has been impressive, there are significant obstacles to overcome before speech recognition systems can reach their full potential. Automatic speech recognition (ASR) systems must be robust to all levels, so that they can handle background or channel noise, the occurrence on unfamiliar words, new accents, new users, or unanticipated inputs. They must exhibit more 'intelligence' and integrate speech with other modalities, deriving the user's intent by combining speech with facial expressions, eye movements, gestures, and other input features, and communicating back to the user through multimedia responses. Therefore, as speech recognition technology is transferred from the laboratory to the marketplace, robustness in recognition becomes increasingly significant. This E-book should be useful to computer engineers interested in recent developments in speech recognition technology.

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