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Proceedings Paper

Real-world speech recognition with neural networks
Author(s): Etienne Barnard; Ronald Cole; Mark Fanty; Pieter J. E. Vermeulen
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Paper Abstract

We describe a system based on neural networks that is designed to recognize speech transmitted through the telephone network. Context-dependent phonetic modeling is studied as a method of improving recognition accuracy, and a special training algorithm is introduced to make the training of these nets more manageable. Our system is designed for real-world applications, and we have therefore specialized our implementation for this goal; a pipelined DSP structure and a compact search algorithm are described as examples of this specialization. Preliminary results from a realistic test of the system (a field trial for the U.S. Census Bureau) are reported.

Paper Details

Date Published: 6 April 1995
PDF: 14 pages
Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); doi: 10.1117/12.205157
Show Author Affiliations
Etienne Barnard, Oregon Graduate Institute (United States)
Ronald Cole, Oregon Graduate Institute (United States)
Mark Fanty, Oregon Graduate Institute (United States)
Pieter J. E. Vermeulen, Oregon Graduate Institute (United States)

Published in SPIE Proceedings Vol. 2492:
Applications and Science of Artificial Neural Networks
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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