Robust Automatic Speech Recognition (notice n° 63498)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02702cam a2200301zu 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | FRCYB88831052 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250107213652.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250107s2015 fr | o|||||0|0|||eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9780128023983 |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | FRCYB88831052 |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | FR-PaCSA |
| Language of cataloging | en |
| Transcribing agency | |
| Description conventions | rda |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Li, Jinyu |
| 245 01 - TITLE STATEMENT | |
| Title | Robust Automatic Speech Recognition |
| Remainder of title | A Bridge to Practical Applications |
| Statement of responsibility, etc. | ['Li, Jinyu', 'Deng, Li', 'Haeb-umbach, Reinhold'] |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Name of producer, publisher, distributor, manufacturer | Elsevier Science |
| Date of production, publication, distribution, manufacture, or copyright notice | 2015 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | p. |
| 336 ## - CONTENT TYPE | |
| Content type code | txt |
| Source | rdacontent |
| 337 ## - MEDIA TYPE | |
| Media type code | c |
| Source | rdamdedia |
| 338 ## - CARRIER TYPE | |
| Carrier type code | c |
| Source | rdacarrier |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Robust Automatic Speech Recognition: A Bridge to Practical Applications establishes a solid foundation for automatic speech recognition that is robust against acoustic environmental distortion. It provides a thorough overview of classical and modern noise-and reverberation robust techniques that have been developed over the past thirty years, with an emphasis on practical methods that have been proven to be successful and which are likely to be further developed for future applications. The strengths and weaknesses of robustness-enhancing speech recognition techniques are carefully analyzed. The book covers noise-robust techniques designed for acoustic models which are based on both Gaussian mixture models and deep neural networks. In addition, a guide to selecting the best methods for practical applications is provided. The reader will: Gain a unified, deep and systematic understanding of the state-of-the-art technologies for robust speech recognitionLearn the links and relationship between alternative technologies for robust speech recognition Be able to use the technology analysis and categorization detailed in the book to guide future technology developmentBe able to develop new noise-robust methods in the current era of deep learning for acoustic modeling in speech recognition The first book that provides a comprehensive review on noise and reverberation robust speech recognition methods in the era of deep neural networksConnects robust speech recognition techniques to machine learning paradigms with rigorous mathematical treatmentProvides elegant and structural ways to categorize and analyze noise-robust speech recognition techniques Written by leading researchers who have been actively working on the subject matter in both industrial and academic organizations for many years |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Li, Jinyu |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Deng, Li |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Haeb-umbach, Reinhold |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Access method | Cyberlibris |
| Uniform Resource Identifier | <a href="https://international.scholarvox.com/netsen/book/88831052">https://international.scholarvox.com/netsen/book/88831052</a> |
| Electronic format type | text/html |
| Host name | |
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