Bark scaled oversampled WPT based speech recognition enhancement in noisy environments

The performance of speech recognition system degrades significantly in real-world environment, is a case of the acoustic mismatch between the training and operating conditions. This paper presents a two-stage approach to make a speech recognition system immune to additive and uncorrelated background noise i.e. robust. In the first stage, an oversampled wavelet packet decomposes the entire input noisy speech into seventeen nonlinear frequency subbands like the Bark scale of the human hearing system and the adaptive noise estimation based spectral subtraction filters the noisy speech from each subband signal. The oversampled WPT is linear and advantageous as it causes to overcome the shift-invariance complexity by removing the decimation after the filtering at each decomposition level. In the second stage, a nonparametric approach is used for feature extraction from filtered speech, and the parameters from the feature extraction stage are compared with the parameters extracted from speech signals stored in a template to recognize the utterance. A series of experiments are carried out to evaluate the performance of the proposed two-stage system in a variety of real environments, with and without the use of the first stage. Recognition accuracy is evaluated at the word level in a wide range of SNRs for various types of noisy environments. The experimental results show significant improvement in recognition performance at low SNR using the proposed system.