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