000			03650nam a2200301za04500
001			17667
008			050703s2011 gw eng d
020			_a9783642213175 99783642213175
082			_a621.382 _b223
245			_aRobust Speech Recognition of Uncertain or Missing Data _h[electronic resource]: _bTheory and Applications / _cedited by Dorothea Kolossa, Reinhold Hab-Umbach.
300			_aXIV, 380p. 69 illus., 17 illus. in color. _bonline resource.
505			_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			_aEngineering. _996
650			_aArtificial intelligence. _933648
650			_aEngineering. _996
650			_aArtificial Intelligence (incl. Robotics). _923200
650			_933860 _aSIGNAL, IMAGE AND SPEECH PROCESSING
650			_934008 _aCOMPUTATIONAL LINGUISTICS
650			_934008 _aCOMPUTATIONAL LINGUISTICS
700			_aKolossa, Dorothea. _935741
700			_eeditor. _935742
700			_aHab-Umbach, Reinhold. _935743
700			_eeditor. _935742
710			_aSpringerLink (Online service) _9111
856			_uhttp://springer.escuelaing.metaproxy.org/book/10.1007/978-3-642-21317-5 _yir a documento _qURL
942			_2ddc _cCF
999			_c14292 _d14292