000			02702cam a2200301zu 4500
001			88831052
003			FRCYB88831052
005			20250107213652.0
006			m o d
007			cr un
008			250107s2015 fr | o|||||0|0|||eng d
020			_a9780128023983
035			_aFRCYB88831052
040			_aFR-PaCSA _ben _c _erda
100	1		_aLi, Jinyu
245	0	1	_aRobust Automatic Speech Recognition _bA Bridge to Practical Applications _c['Li, Jinyu', 'Deng, Li', 'Haeb-umbach, Reinhold']
264		1	_bElsevier Science _c2015
300			_a p.
336			_btxt _2rdacontent
337			_bc _2rdamdedia
338			_bc _2rdacarrier
650		0	_a
700	0		_aLi, Jinyu
700	0		_aDeng, Li
700	0		_aHaeb-umbach, Reinhold
856	4	0	_2Cyberlibris _uhttps://international.scholarvox.com/netsen/book/88831052 _qtext/html _a
520			_aRobust Automatic Speech Recognition: A Bridge to Practical Applications establishes a solid foundation for automatic speech recognition that is robust against acoustic environmental distortion. It provides a thorough overview of classical and modern noise-and reverberation robust techniques that have been developed over the past thirty years, with an emphasis on practical methods that have been proven to be successful and which are likely to be further developed for future applications. The strengths and weaknesses of robustness-enhancing speech recognition techniques are carefully analyzed. The book covers noise-robust techniques designed for acoustic models which are based on both Gaussian mixture models and deep neural networks. In addition, a guide to selecting the best methods for practical applications is provided. The reader will: Gain a unified, deep and systematic understanding of the state-of-the-art technologies for robust speech recognitionLearn the links and relationship between alternative technologies for robust speech recognition Be able to use the technology analysis and categorization detailed in the book to guide future technology developmentBe able to develop new noise-robust methods in the current era of deep learning for acoustic modeling in speech recognition The first book that provides a comprehensive review on noise and reverberation robust speech recognition methods in the era of deep neural networksConnects robust speech recognition techniques to machine learning paradigms with rigorous mathematical treatmentProvides elegant and structural ways to categorize and analyze noise-robust speech recognition techniques Written by leading researchers who have been actively working on the subject matter in both industrial and academic organizations for many years
999			_c63498 _d63498