Robust Speech Recognition of Uncertain or Missing Data (Record no. 107952)

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001 - CONTROL NUMBER
control field 978-3-642-21317-5
003 - CONTROL NUMBER IDENTIFIER
control field DE-He213
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20140220083804.0
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
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fixed length control field 110712s2011 gw | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9783642213175
-- 978-3-642-21317-5
024 7# - OTHER STANDARD IDENTIFIER
Standard number or code 10.1007/978-3-642-21317-5
Source of number or code doi
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number TK5102.9
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number TA1637-1638
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number TK7882.S65
072 #7 - SUBJECT CATEGORY CODE
Subject category code TTBM
Source bicssc
072 #7 - SUBJECT CATEGORY CODE
Subject category code UYS
Source bicssc
072 #7 - SUBJECT CATEGORY CODE
Subject category code TEC008000
Source bisacsh
072 #7 - SUBJECT CATEGORY CODE
Subject category code COM073000
Source bisacsh
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 621.382
Edition number 23
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Kolossa, Dorothea.
Relator term editor.
245 10 - TITLE STATEMENT
Title Robust Speech Recognition of Uncertain or Missing Data
Medium [electronic resource] :
Remainder of title Theory and Applications /
Statement of responsibility, etc edited by Dorothea Kolossa, Reinhold Häb-Umbach.
264 #1 -
-- Berlin, Heidelberg :
-- Springer Berlin Heidelberg,
-- 2011.
300 ## - PHYSICAL DESCRIPTION
Extent XIV, 380p. 69 illus., 17 illus. in color.
Other physical details online resource.
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-- computer
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-- online resource
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-- text file
-- PDF
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505 0# - FORMATTED CONTENTS NOTE
Formatted contents note Chap. 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 ## - SUMMARY, ETC.
Summary, etc Automatic 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 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Engineering.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Artificial intelligence.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computational linguistics.
650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Engineering.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Signal, Image and Speech Processing.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Artificial Intelligence (incl. Robotics).
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computational Linguistics.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Häb-Umbach, Reinhold.
Relator term editor.
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element SpringerLink (Online service)
773 0# - HOST ITEM ENTRY
Title Springer eBooks
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Printed edition:
International Standard Book Number 9783642213168
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier http://dx.doi.org/10.1007/978-3-642-21317-5
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-- ZDB-2-ENG

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