Speech recognition in noise using a self-structuring noise reduction model and hidden control models

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Abstract

This paper describes how speech recognition in the presence of F-16 jet cockpit noise can be performed using a sequence of three units - an auditory model and two neural models. A method for noise reduction in the cepstral domian based on a self-structuring universal approximator is proposed and tested on a large database of isolated words contaminated with jet noise. This approach is a potential alternative to traditional recognition methods for noisy speech and makes noise reduction possible in all three models as in the system in [l]. The first model performs a spectral analysis of the input speech signal. The second model is a Self-structuring Neural Noise Reduction (SNNR) model, which is an alternative to the noise reduction model [l] presented at IJCNN91. The noise reduced output from the SNNR network is propagated through the speech recognizer consisting of a set of Hidden Control Neural Networks (HCNN).
Original languageEnglish
Title of host publicationIJCNN-92
Volume2
PublisherIEEE Press
Publication date1992
Pages279-284
ISBN (Print)0-7803-0559-0
DOIs
Publication statusPublished - 1992
Externally publishedYes
Event1992 International Joint Conference on Neural Networks - Baltimore, MD, United States
Duration: 7 Jun 199211 Jun 1992

Conference

Conference1992 International Joint Conference on Neural Networks
Country/TerritoryUnited States
CityBaltimore, MD
Period07/06/199211/06/1992

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