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Proceedings Paper

Neural network model for isolated-utterance speech recognition
Author(s): Jung H. Kim; Thomas Ervin; Evi H. Park; Celestine A. Ntuen; Shiu M. Cheung; Wagih H. Makky
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Paper Abstract

Speech recognition by machine has finally come of age in a practical sense. A major problem in speech recognition, however, stems from the large variance of different utterances for the same word. This paper proposes an efficient method of achieving high accuracy speaker- independent isolated-word recognition through the implementation of associative memories and neural networks. The basic architecture of such a process involves two-stages: speech analysis and recognition.

Paper Details

Date Published: 1 February 1994
PDF: 12 pages
Proc. SPIE 2093, Substance Identification Analytics, (1 February 1994); doi: 10.1117/12.172532
Show Author Affiliations
Jung H. Kim, North Carolina A&T State Univ. (United States)
Thomas Ervin, North Carolina A&T State Univ. (United States)
Evi H. Park, North Carolina A&T State Univ. (United States)
Celestine A. Ntuen, North Carolina A&T State Univ. (United States)
Shiu M. Cheung, FAA Technical Ctr. (United States)
Wagih H. Makky, FAA Technical Ctr. (United States)

Published in SPIE Proceedings Vol. 2093:
Substance Identification Analytics
James L. Flanagan; Richard J. Mammone; Albert E. Brandenstein; Edward Roy Pike; Stelios C. A. Thomopoulos; Marie-Paule Boyer; H. K. Huang; Osman M. Ratib, Editor(s)

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