Robust Speech Recognition of Uncertain or Missing Data [recurso electrónico] : Theory and Applications / edited by Dorothea Kolossa, Reinhold Häb-Umbach.

Por: Kolossa, Dorothea [editor.]Colaborador(es): Häb-Umbach, Reinhold [editor.] | SpringerLink (Online service)Tipo de material: TextoTextoEditor: Berlin, Heidelberg : Springer Berlin Heidelberg, 2011Descripción: XVIII, 380 p. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9783642213175Tema(s): Engineering | Artificial intelligence | Computational linguistics | Engineering | Signal, Image and Speech Processing | Artificial Intelligence (incl. Robotics) | Computational LinguisticsFormatos físicos adicionales: Printed edition:: Sin títuloClasificación CDD: 621.382 Clasificación LoC:TK5102.9TA1637-1638TK7882.S65Recursos en línea: Libro electrónicoTexto
Contenidos:
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.
En: Springer eBooksResumen: 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.  
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Libro Electrónico Biblioteca Electrónica
Colección de Libros Electrónicos TK5102.9 (Browse shelf(Abre debajo)) 1 No para préstamo 376246-2001

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.

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.  

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