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Part of the book series: The Springer International Series in Engineering and Computer Science (SECS, volume 247)
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Table of contents (16 chapters)
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Front Matter
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Background
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Front Matter
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Additional Topics
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Front Matter
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Finale
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Front Matter
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About this book
The book describes a successful five-year international collaboration between the authors. The lessons learned form a case study that demonstrates how hybrid systems can be developed to combine neural networks with more traditional statistical approaches. The book illustrates both the advantages and limitations of neural networks in the framework of a statistical systems.
Using standard databases and comparison with some conventional approaches, it is shown that MLP probability estimation can improve recognition performance. Other approaches are discussed, though there is no such unequivocal experimental result for these methods.
Connectionist Speech Recognition is of use to anyone intending to use neural networks for speech recognition or within the framework provided by an existing successful statistical approach. This includes research and development groups working in the field of speech recognition, both with standard and neural network approaches, as well as other pattern recognition and/or neural network researchers. The book is also suitable as a text for advanced courses on neural networks or speech processing.
Authors and Affiliations
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Lernout & Hauspie Speech Products, Belgium
Hervé A. Bourlard
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International Computer Science Institute, Berkeley, USA
Hervé A. Bourlard, Nelson Morgan
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University of California, Berkeley, USA
Nelson Morgan
Bibliographic Information
Book Title: Connectionist Speech Recognition
Book Subtitle: A Hybrid Approach
Authors: Hervé A. Bourlard, Nelson Morgan
Series Title: The Springer International Series in Engineering and Computer Science
DOI: https://doi.org/10.1007/978-1-4615-3210-1
Publisher: Springer New York, NY
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eBook Packages: Springer Book Archive
Copyright Information: Springer Science+Business Media New York 1994
Hardcover ISBN: 978-0-7923-9396-2Published: 31 October 1993
Softcover ISBN: 978-1-4613-6409-2Published: 15 December 2012
eBook ISBN: 978-1-4615-3210-1Published: 06 December 2012
Series ISSN: 0893-3405
Edition Number: 1
Number of Pages: XXIX, 313
Topics: Circuits and Systems, Complex Systems, Signal, Image and Speech Processing, Electrical Engineering, Statistical Physics and Dynamical Systems