A self-structuring neural noise reduction model

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Abstract

The authors describe 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 domain 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. The first model performs a spectral analysis of the input speech signal. The second model is a self-structuring neural noise reduction (SNNR) model. 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 publicationEUROSPEECH 91
Publication date1990
Publication statusPublished - 1990
Externally publishedYes
EventESCA Proceedings European Conference on Speech Communication and Technology - Genova, Italy
Duration: 1 Jan 1991 → …

Conference

ConferenceESCA Proceedings European Conference on Speech Communication and Technology
CityGenova, Italy
Period01/01/1991 → …

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