A Noise Robust Speech Recognition Method Using Model Compensation Based on Speech Enhancement

In this paper, we propose a MWF-PMC noise processing method which enhances the input speech by using Mel-warped Wiener Filtering (MWF) at pre-processing stage and compensates the recognition model by using PMC (Parallel Model Combination) at post processing stage for speech recognition in noisy environments. The PMC uses the residual noise extracted from the silence region of enhanced speech at pre-processing stage to compensate the clean speech model and thus this method is considered to improve the performance of speech recognition in noisy environments. For recognition experiments we down sampled KLE PBW (Phoneme Balanced Words) 452 word speech data to 8 kHz and made 5 different SNR levels of noisy speech, i.e., 0 dB, 5 dB, 10 dB, 15 dB and 20 dB, by adding Subway, Car and Exhibition noise to clean speech. From the recognition results, we could confirm the effectiveness of the proposed MWF PMC method by obtaning the improved recognition performances over all compared with the existing combined methods.