Noise reduction for speech recognition

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395 242, 395 23, 395 24, G10L 506, G10L 900

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055839681

ABSTRACT:
A neural network for noise reduction for speech recognition in a noisy environment uses an algorithm for automatic network generation which automatically selects a suitable signal representation. Nodes may be added to the input layer of the neural network successively, with a new node being trained by calculating and minimizing a mapping error. A squared mapping error may be formed and the mapping error may be assigned a weight dependent on the importance of the vectors. In addition, a neural network that performs neural noise reduction by reducing, in a training phase, a mapping error between noise-free vectors at an output of the neural network and noise-reduced vectors at the output of the neural network using an iterative process, has the mapping error further reduced by additional information which is selected from a suitable signal representation at the input of the neural network.

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"A Cepstral Noise Reduction Multi-Layer Neural Network", H. Sorenson, ICASSP91, Toronto, Canada, May 14-17, 1991, pp. 933-936.
"Self-Structuring Neural Noise Reduction Model", H. Sorenson et al, University of Aalberg, Denmark, after May 1991, pp. 567-570.
"Noise Reduction Using Connectionist Models", S. Tamura et al, ICASSP '88, Osaka, Japan, Apr. 1988.
"Building Blocks for a Neural Noise Reduction Network for Robust Speech Recognition", M. Trompf, Proceedings of EUSIPCA 1992, Brussels, Belgium, Aug. 24-27, 1992.
A cepstral noise reduction multi-layer neural network Sorenson, ICASSP 91. 1991 International Conf. on Acoustics/14-17 May 1991, pp. 933-936.
Neural Network development for noise reduction in robust speech recognition Trompf, IJCNN International Joint Conf. on NN/7-11 Jun. 1992, pp. IV-722 to IV-727.
Improvements to the noise reduction Neural Network Tamura et al., ICASSP 90. 1990 International Conf. on Acoustics/3-6 Apr. 1990, pp. 825-828.

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