MLP network for enhancement of noisy MFCC vectors

The performance of voice dialling systems often degrades rapidly as the intensity of the background noise increases. In this paper, we describe a neural network based speech enhancement technique for improving the speech recognition performance of a voice dialling system in very noisy real world type conditions. The speech samples were recorded in laboratory conditions and afterwards corrupted by adding car noise or babble noise recorded in a cafe. These noise corrupted speech samples were enhanced in cepstral domain by a context dependent multilayer perceptron (MLP) network before performing the recognition using a hidden Markov model (HMM) based speech recognition system. The accuracy of the test set increased 58%, 55% and 46% in the car noise environments having -5 dB, 0 dB and 5 dB SNRs, respectively. The accuracy of the test set increased 44%, 48% and 39% in the babble noise environments having SNR 5 dB, 10 dB and 15 dB, respectively. The accuracy remained approximately same for both car and babble noise environments when having SNR of 20 dB.