Editors: Javier Ramírez, Juan Manuel Górriz

Recent Advances in Robust Speech Recognition Technology

eBook: US $21 Special Offer (PDF + Printed Copy): US $129
Printed Copy: US $119
Library License: US $84
ISBN: 978-1-60805-389-6 (Print)
ISBN: 978-1-60805-172-4 (Online)
Year of Publication: 2011
DOI: 10.2174/9781608051724111010


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.


- Pp. i
Alex Acero
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- Pp. ii
Javier Ramirez, Juan Manuel Gorriz
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- Pp. iii-vi (4)
Javier Ramirez, Juan Manuel Gorriz
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Integration of Statistical-Model-Based Voice Activity Detection and Noise Suppression for Noise Robust Speech Recogni

- Pp. 1-12 (12)
Masakiyo Fujimoto
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Using GARCH Process for Voice Activity Detection

- Pp. 13-29 (17)
Rasool Tahmasbi
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Voice Activity Detection Using Contextual Information for Robust Speech Recognition

- Pp. 30-45 (16)
J. Ramirez, J. M. Gorriz
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Improved Long term Voice Activity Detection for Robust Speech Recognition

- Pp. 46-59 (14)
Juan M. Gorriz, Javier Ramirez
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Speech Enhancement Algorithms: A Survey

- Pp. 60-102 (43)
Philipos C. Loizou
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Speech Enhancement and Representation Employing the Independent Component Analysis

- Pp. 103-113 (11)
Peter Jancovic, Xin Zou, Munevver Kokuer
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Statistical Model based Techniques for Robust Speech Communication

- Pp. 114-132 (19)
Nam Soo Kim, Joon-Hyuk Chang
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Bayesian Networks and Discrete Observations for Robust Speech Recognition

- Pp. 133-140 (8)
Antonio Miguel, Alfonso Ortega, Eduardo Lleida
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Robust Large Vocabulary Continuous Speech Recognition Based on Missing Feature Techniques

- Pp. 141-154 (14)
Yujun Wang, Maarten Van Segbroeck, Hugo Van hamme
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Distribution-Based Feature Compensation for Robust Speech Recognition

- Pp. 155-168 (14)
Berlin Chen, Shih-Hsiang Lin
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Effective Multiple Regression for Robust Singleand Multi-channel Speech Recognition

- Pp. 169-174 (6)
Weifeng Li, Kazuya Takeda, Fumitada Itakura
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Higher Order Cepstral Moment Normalization for Improved Robust Speech Recognition

- Pp. 175-189 (15)
Chang-Wen Hsu, Lin-Shan Lee
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Reviewing Feature Non-Linear Transformations for Robust Speech Recognition

- Pp. 190-196 (7)
Luz Garcia, Jose Carlos Segura, Angel de la Torre
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Advances in Human-Machine Systems for In-Vehicle Environments: Noise and Cognitive Stress/Distraction

- Pp. 197-210 (14)
John H.L. Hansen, Pongtep Angkititrakul, Wooil Kim
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- Pp. 211-214 (4)
Javier Ramirez, Juan Manuel Gorriz
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