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TMCNet:  Recent Advances in Robust Speech Recognition Technology

[January 04, 2013]

Recent Advances in Robust Speech Recognition Technology

Jan 04, 2013 (M2 PRESSWIRE via COMTEX) -- Research and Markets (http://www.researchandmarkets.com/research/gdbmsb/recent_advances) has announced the addition of the "Recent Advances in Robust Speech Recognition Technology" book to their offering.


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.

Key Topics Covered: Section I. Voice activity detection 1. Integration of statistical model-based voice activity detection and noise suppression for noise robust speech recognition 2. Using GARCH Process for Voice Activity Detection 3. Voice activity detection using contextual information for robust speech recognition 4. Improved Long term Voice Activity Detection for Robust Speech Recognition Section II. Speech enhancement 5. Speech enhancement algorithms: A survey 6. Speech enhancement and representation employing the independent componentanalysis 7. Statistical Model based Techniques for Robust Speech Communication Section III. Speech recognition 8. Bayesian Networks and Discrete Observations for Robust Speech Recognition 9. Robust Large Vocabulary Continuous Speech Recognition Based on Missing FeatureTechniques 10. Distribution-Based Feature Compensation for Robust Speech Recognition 11. Effective Multiple Regression for Robust Single- and Multichannel SpeechRecognition 12.Higher Order Cepstral Moment Normalization for Improved Robust Speech Recognition 13. Reviewing Feature Non-Linear Transformations for Robust Speech Recognition 14. Advances in Human-Machine Systems for In-Vehicle Environments: Noise and Cognitive Stress/Distraction For more information visit http://www.researchandmarkets.com/research/gdbmsb/recent_advances CONTACT: Research and Markets, Laura Wood, Senior Manager.

press@researchandmarkets.com Fax from USA: 646-607-1907 Fax from rest of the world: +353-1-481-1716 Sector: Computing (http://www.researchandmarkets.com/categories.asp cat_id=32&campaign_id=gdbmsb), Engineering (http://www.researchandmarkets.com/categories.asp cat_id=50&campaign_id=gdbmsb) ((M2 Communications disclaims all liability for information provided within M2 PressWIRE. Data supplied by named party/parties. Further information on M2 PressWIRE can be obtained at http://www.presswire.net on the world wide web. Inquiries to info@m2.com.

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