Conference paper
Speech recognition in noise using a self-structuring noise reduction model and hidden control models
The author describes how speech recognition in the presence of F-16 jet cockpit noise can be performed using a sequence of three units, i.e. 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, which is an alternative to the noise reduction 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). The author concludes that the SNNR network is a very powerful method for noise reduction in general and that the preliminary results presented can be improved.<>
| Language: | English |
|---|---|
| Year: | 1992 |
| Pages: | 279,280,281,282,283,284 |
| ISBN: | 0780305590 and 9780780305595 |
| Types: | Conference paper |
| DOI: | 10.1109/IJCNN.1992.226995 |
Cepstral analysis F-16 jet cockpit noise Filtering Helium Multi-layer neural network Neural networks Noise reduction Signal mapping Speech analysis Speech enhancement Speech recognition auditory model cepstral domain hidden Markov models hidden control models neural nets neural networks self-structuring noise reduction model self-structuring universal approximator spectral analysis speech recognition
