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Proceedings Paper

Recovery of partially occluded speech segments using Hopfield neural network
Author(s): Ismail I. Jouny; Brian MacDonald
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Paper Abstract

This paper focuses on utilizing the associative capabilities of the Hopfield neural net in processing digitized speech and recovering erroneous speech segments and reconstructing noisy speech. The scope of this study is limited and the tests conducted are exploratory in nature. However, with a limited vocabulary that fits many practical applications, this study shows that digitized speech can be enhanced using properly trained recurrent networks such as the Hopfield neural net. The results indicate that a Hopfield neural network with sufficient associative memory can be used in a limited vocabulary context to reconstruct digitized speech with noisy, erroneous, and occluded or silenced segments.

Paper Details

Date Published: 17 August 2000
PDF: 7 pages
Proc. SPIE 4050, Automatic Target Recognition X, (17 August 2000); doi: 10.1117/12.395557
Show Author Affiliations
Ismail I. Jouny, Lafayette College (United States)
Brian MacDonald, Lafayette College (United States)

Published in SPIE Proceedings Vol. 4050:
Automatic Target Recognition X
Firooz A. Sadjadi, Editor(s)

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