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Robust Speech Recognition of Uncertain or Missing Data Theory and Applications /
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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor Corporativo: | |
| Otros Autores: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2011.
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| Edición: | 1st ed. 2011. |
| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
| LEADER | 00000nam a22000005i 4500 | ||
|---|---|---|---|
| 001 | 978-3-642-21317-5 | ||
| 003 | DE-He213 | ||
| 005 | 20220119095028.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 110712s2011 gw | s |||| 0|eng d | ||
| 020 | |a 9783642213175 |9 978-3-642-21317-5 | ||
| 024 | 7 | |a 10.1007/978-3-642-21317-5 |2 doi | |
| 050 | 4 | |a TK5102.9 | |
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| 245 | 1 | 0 | |a Robust Speech Recognition of Uncertain or Missing Data |h [electronic resource] : |b Theory and Applications / |c edited by Dorothea Kolossa, Reinhold Haeb-Umbach. |
| 250 | |a 1st ed. 2011. | ||
| 264 | 1 | |a Berlin, Heidelberg : |b Springer Berlin Heidelberg : |b Imprint: Springer, |c 2011. | |
| 300 | |a XVIII, 380 p. |b online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file |b PDF |2 rda | ||
| 505 | 0 | |a 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 | |a 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 | |a Signal processing. | |
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 0 | |a Computational linguistics. | |
| 650 | 1 | 4 | |a Signal, Speech and Image Processing . |
| 650 | 2 | 4 | |a Artificial Intelligence. |
| 650 | 2 | 4 | |a Computational Linguistics. |
| 700 | 1 | |a Kolossa, Dorothea. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Haeb-Umbach, Reinhold. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9783642213168 |
| 776 | 0 | 8 | |i Printed edition: |z 9783642438684 |
| 776 | 0 | 8 | |i Printed edition: |z 9783642213182 |
| 856 | 4 | 0 | |u https://doi.uam.elogim.com/10.1007/978-3-642-21317-5 |z Texto Completo |
| 912 | |a ZDB-2-ENG | ||
| 912 | |a ZDB-2-SXE | ||
| 950 | |a Engineering (SpringerNature-11647) | ||
| 950 | |a Engineering (R0) (SpringerNature-43712) | ||