Robust in-car speech recognition based on nonlinear multiple regressions
EURASIP Journal on Applied Signal Processing
A Neural Network Based Regression Approach for Recognizing Simultaneous Speech
MLMI '08 Proceedings of the 5th international workshop on Machine Learning for Multimodal Interaction
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The problem of speech recognition in the presence of interfering nonstationary noise is addressed. A method for noise reduction in the cepstral domain based on a multilayer network is proposed and tested on a large database of isolated words contaminated with nonstationary F-16 jet noise. The speech recognition system consists of an auditory preprocessing module, the cepstral noise reduction multilayer network, and a neural network classifier. The noise reduction network performs a nonlinear autoassociative mapping in the cepstral domain between a set of noisy cepstral coefficients and a set of noise-free cepstral coefficients. The average recognition rate on a test database was improved up to 65% when the noise reduction network was added to the speech recognition system.