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A Robust Front-End Processor combining Mel Frequency Cepstral Coefficient and Sub-band Spectral Centroid Histogram methods for Automatic Speech Recognition

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Author(s): R. Thangarajan | A.M. Natarajan

Journal: International Journal of Signal Processing, Image Processing and Pattern Recognition
ISSN 2005-4254

Volume: 2;
Issue: 2;
Start page: 67;
Date: 2009;
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Keywords: Auditory models | dominant-frequencies | feature extraction | MFCC | Noise robustness | SSCH

ABSTRACT
Environmental robustness is an important area of research in speech recognition. Mismatch between trained speech models and actual speech to be recognized is due to factors like background noise. It can cause severe degradation in the accuracy of recognizers whichare based on commonly used features like mel-frequency cepstral co-efficient (MFCC) and linear predictive coding (LPC). It is well understood that all previous auditory based feature extraction methods perform extremely well in terms of robustness due to the dominantfrequency information present in them. But these methods suffer from high computational cost. Another method called sub-band spectral centroid histograms (SSCH) integrates dominant-frequency information with sub-band power information. This method is based onsub-band spectral centroids (SSC) which are closely related to spectral peaks for both clean and noisy speech. Since SSC can be computed efficiently from short-term speech power spectrum estimate, SSCH method is quite robust to background additive noise at a lowercomputational cost. It has been noted that MFCC method outperforms SSCH method in the case of clean speech. However in the case of speech with additive noise, MFCC method degrades substantially. In this paper, both MFCC and SSCH feature extraction have beenimplemented in Carnegie Melon University (CMU) Sphinx 4.0 and trained and tested on AN4 database for clean and noisy speech. Finally, a robust speech recognizer which automatically employs either MFCC or SSCH feature extraction methods based on the variance of shortterm power of the input utterance is suggested.
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