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

IEEE/ACM transactions on audio, speech, and language processing, 2016-07, Vol.24 (7), p.1315-1329 [Peer Reviewed Journal]

ISSN: 2329-9290 ;EISSN: 2329-9304 ;DOI: 10.1109/TASLP.2016.2545928 ;CODEN: ITASD8

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  • Title:
    Power-Normalized Cepstral Coefficients (PNCC) for Robust Speech Recognition
  • Author: Chanwoo Kim ; Stern, Richard M.
  • Subjects: asymmetric filtering ; Feature extraction ; IEEE transactions ; medium-time power estimation ; Mel frequency cepstral coefficient ; modulation filtering ; online speech processing ; physiological modeling ; power function ; rate-level curve ; Robust speech recognition ; spectral weight smoothing ; Speech ; Speech processing ; Speech recognition ; temporal masking
  • Is Part Of: IEEE/ACM transactions on audio, speech, and language processing, 2016-07, Vol.24 (7), p.1315-1329
  • Description: This paper presents a new feature extraction algorithm 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 suppresses background excitation, and a module that accomplishes temporal masking. We also propose the use of medium-time power analysis in which environmental parameters 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.
  • Publisher: IEEE
  • Language: English
  • Identifier: ISSN: 2329-9290
    EISSN: 2329-9304
    DOI: 10.1109/TASLP.2016.2545928
    CODEN: ITASD8
  • Source: IEEE Open Access Journals
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