Recovery of partially occluded speech segments using Hopfield neural network

Ismail I. Jouny; Brian MacDonald

doi:10.1117/12.395557

17 August 2000 Recovery of partially occluded speech segments using Hopfield neural network

Ismail I. Jouny, Brian MacDonald

Proceedings Volume 4050, Automatic Target Recognition X; (2000) https://doi.org/10.1117/12.395557
Event: AeroSense 2000, 2000, Orlando, FL, United States

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.

Citation Download Citation

Ismail I. Jouny and Brian MacDonald "Recovery of partially occluded speech segments using Hopfield neural network", Proc. SPIE 4050, Automatic Target Recognition X, (17 August 2000); https://doi.org/10.1117/12.395557

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