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...
Gespeichert in:
| Weitere Verfasser: | , |
|---|---|
| Format: | Livre numérique |
| Sprache: | Anglais |
| Veröffentlicht: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
[20..].
Cham : Springer Nature |
| Ausgabe: | 1st ed. 2011. |
| Online Zugang: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
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Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Robust speech recognition of uncertain or missing data, theory and applications, Dorothea Kolossa, Reinhold Haeb-Umbach, New York, Springer, 2011, 1 vol. (XVIII-380 p.), 978-3-642-21316-8 |
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| 245 | 0 | 0 | |a Robust speech recognition of uncertain or missing data : |b theory and applications |c edited by Dorothea Kolossa, Reinhold Häb-Umbach. |
| 250 | |a 1st ed. 2011. | ||
| 260 | |a Berlin, Heidelberg : |b Springer Berlin Heidelberg. | ||
| 260 | |a Cham : |b Springer Nature, |c [20..]. | ||
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 505 | 1 | |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 | |
| 506 | |a Accès en ligne pour les établissements français bénéficiaires des licences nationales | ||
| 506 | |a Accès soumis à abonnement pour tout autre établissement | ||
| 506 | |a Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 | ||
| 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. | ||
| 700 | 1 | |a Kolossa, Dorothea. |4 pbd | |
| 700 | 1 | |a Häb-Umbach, Reinhold, |d 19..- |4 pbd | |
| 776 | 0 | |0 157167453 |t Robust speech recognition of uncertain or missing data |o theory and applications |f Dorothea Kolossa, Reinhold Haeb-Umbach |c New York |n Springer |d 2011 |p 1 vol. (XVIII-380 p.) |z 978-3-642-21316-8 | |
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