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Power-Normalized Cepstral Coefficients (PNCC) for Robust Speech Recognition


POWER-NORMALIZED CEPSTRAL COEF

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Abstract


This paper presents a new feature extraction algo-rithm called Power Normalized Cepstral Coefficients (PNCC) that is motivated by auditory processing. Major new features of PNCC processing include the use of a power-law nonlinearity that replaces the traditional log nonlinearity used in MFCC coefficients, a noise-suppression algorithm based on asymmetric filtering that suppress background excitation, and a module that accomplishes temporal masking. We also propose the use of medium-time power analysis, in which environmental param-eters are estimated over a longer duration than is commonly used for speech, as well as frequency smoothing. Experimental results demonstrate that PNCC processing provides substantial improvements in recognition accuracy compared to MFCC and PLP processing for speech in the presence of various types of additive noise and in reverberant environments, with only slightly greater computational cost than conventional MFCC processing, and without degrading the recognition accuracy that is observed while training and testing using clean speech. PNCC processing also provides better recognition accuracy in noisy environments than techniques such as Vector Taylor Series (VTS) and the ETSI Advanced Front End (AFE) while requiring much less computation. We describe an implementation of PNCC using ‚??online processing‚?? that does not require future knowledge of the input

KeyWords
Robust speech recognition, feature extraction, physiological modeling, ratelevel curve, power function, asymmetric filtering, mediumtime power estimation, spectral weight smoothing, temporal masking, modulation filtering, on line speech processing



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