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• Where represents the power spectrum of the degraded speech, is the power spectrum of the clean speech, is the transfer function of the linear filter, and is the power spectrum of the additive noise.) (|) (|) () Z(2 ω ω ω ω N H X + =) Z(ω) X(ω) (ω H) (ω N • In the log-Spectral domain this relation can be expressed as:) 1 log(q x n e q x z − − + + + = of in(More)
Speech recognition systems perform poorly in the presence of corrupting noise. Missing feature methods attempt to compensate for the noise by removing noise corrupted components of spectrographic representations of noisy speech and performing recognition with the remaining reliable components. Conventional classifier-compensation methods modify the(More)
This paper presents a new robust feature extraction algorithm based on a modified approach to power bias subtraction combined with applying a threshold to the power spectral density. Power bias level is selected as a level above which the signal power distribution is sharpest. The sharpness is measured using the ratio of arithmetic mean to the geometric(More)
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(More)
In this paper we describe and compare the performance of a series of cepstrum-based procedures that enable the CMU SPHINX-II speech recognition system to maintain a high level of recognition accuracy over a wide variety of acoustical environments. We describe the MFCDCN algorithm, an environment-independent extension of the efficient SDCN and FCDCN(More)
We present an algorithm for dereverberation of speech signals for automatic speech recognition (ASR) applications. Often ASR systems are presented with speech that has been recorded in environments that include noise and reverberation. The performance of ASR systems degrades with increasing levels of noise and reverberation. While many algorithms have been(More)
This paper presents a new feature extraction algorithm called PNCC that is based on auditory. Major new features of PNCC processing include the use of a power-law nonlinearity that replaces the traditional log nonlinearity used in MFCC coefficients , and a novel algorithm to suppress background excita-tion using medium-duration power estimation based on the(More)
This paper investigates the use of higher-order autoregressive vector predictors for tracking the noise in noisy speech signals. The autoregressive predictors form the state equation of a linear dynamical system that models the spectral dynamics of the noise process. Experiments show that the use of such models to track noise can lead to large gains in(More)
Missing feature methods of noise compensation for speech recognition operate by first identifying components of a spectrographic representation of speech that are considered to be corrupt. Recognition is then performed either using only the remaining reliable components, or the corrupt components are reconstructed prior to recognition. These methods require(More)
Speech recognition performance degrades significantly in distant-talking environments, where the speech signals can be severely distorted by additive noise and reverberation. In such environments, the use of microphone arrays has been proposed as a means of improving the quality of captured speech signals. Currently, microphone-array-based speech(More)