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008			110712s2011 gw | s |||| 0|eng d
020			_a9783642213175 _9978-3-642-21317-5
040			_cMX-MeUAM
050		4	_aTK5102.9
050		4	_aTA1637-1638
050		4	_aTK7882.S65
082	0	4	_a621.382 _223
100	1		_aKolossa, Dorothea. _eeditor.
245	1	0	_aRobust Speech Recognition of Uncertain or Missing Data _h[recurso electrónico] : _bTheory and Applications / _cedited by Dorothea Kolossa, Reinhold Häb-Umbach.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2011.
300			_aXVIII, 380 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aChap. 1 – Introduction -- Part I – Theoretical Foundations -- Chap. 2 – Uncertainty Decoding and Conditional Bayesian Estimation -- Chap. 3 – Uncertainty Propagation -- Part II – Applications -- Chap. 4 – Front-End, Back-End, and Hybrid Techniques for Noise-Robust Speech Recognition -- Chap. 5 – Model-Based Approaches to Handling Uncertainty -- Chap. 6 – Reconstructing Noise-Corrupted Spectrographic Components for Robust Speech Recognition -- Chap. 7 – Automatic Speech Recognition Using Missing Data Techniques: Handling of Real-World Data -- Chap. 8 – Conditional Bayesian Estimation Employing a Phase-Sensitive Estimation Model for Noise-Robust Speech Recognition.-   Part III – Reverberation Robustness -- Chap. 9 – Variance Compensation for Recognition of Reverberant Speech with Dereverberation Processing -- Chap. 10 – A Model-Based Approach to Joint Compensation of Noise and Reverberation for Speech Recognition -- Part IV – Applications: Multiple Speakers and Modalities -- Chap. 11 – Evidence Modelling for Missing Data Speech Recognition Using Small Microphone Arrays -- Chap. 12 – Recognition of Multiple Speech Sources Using ICA.- Chap. 13 – Use of Missing and Unreliable Data for Audiovisual Speech Recognition.-   Index.
520			_aAutomatic speech recognition suffers from a lack of robustness with respect to noise, reverberation and interfering speech. The growing field of speech recognition in the presence of missing or uncertain input data seeks to ameliorate those problems by using not only a preprocessed speech signal but also an estimate of its reliability to selectively focus on those segments and features that are most reliable for recognition. This book presents the state of the art in recognition in the presence of uncertainty, offering examples that utilize uncertainty information for noise robustness, reverberation robustness, simultaneous recognition of multiple speech signals, and audiovisual speech recognition. The book is appropriate for scientists and researchers in the field of speech recognition who will find an overview of the state of the art in robust speech recognition, professionals working in speech recognition who will find strategies for improving recognition results in various conditions of mismatch, and lecturers of advanced courses on speech processing or speech recognition who will find a reference and a comprehensive introduction to the field. The book assumes an understanding of the fundamentals of speech recognition using Hidden Markov Models.
650		0	_aEngineering.
650		0	_aArtificial intelligence.
650		0	_aComputational linguistics.
650	1	4	_aEngineering.
650	2	4	_aSignal, Image and Speech Processing.
650	2	4	_aArtificial Intelligence (incl. Robotics).
650	2	4	_aComputational Linguistics.
700	1		_aHäb-Umbach, Reinhold. _eeditor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783642213168
856	4	0	_zLibro electrónico _uhttp://148.231.10.114:2048/login?url=http://link.springer.com/book/10.1007/978-3-642-21317-5
596			_a19
942			_cLIBRO_ELEC
999			_c204126 _d204126