Lateral inhibition net and weighted matching algorithms for speech recognition in noise

Abstract

The authors address the problem of speech recognition with signals corrupted by white Gaussian additive noise at moderate SNR. The energy of the noise is not required. A technique based on a lateral inhibition process approximation with a multilayer neural net (the lateral inhibition net (LIN)) and neural net processing efficacy weighting in acoustic pattern matching algorithms is proposed. In the recognition procedure, the local SNR is computed by means of the autocorrelation function and is employed to estimate the efficacy of LIN in noise cancelling which is taken into account as a weight in a pattern matching algorithm. A general criterion based on weighting the frame influence in decisions according to the reliability in noise reduction is suggested, and modified versions of both HMM and DTW algorithms have been designed. To be more coherent with the conditions that define LIN, a modification in the backpropagation algorithm is also proposed.