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 excitation using medium-duration power estimation based on the ratio of the arithmetic mean to the geo...

The most popular speech feature extractor used in automatic speech recognition (ASR) systems today is the mel frequency cepstral coefficient (mfcc) algorithm. Introduced in 1980, the filter bank-based algorithm eventually replaced linear prediction cepstral coefficients (lpcc) as the premier front end, primarily because of mfcc’s superior robustness to additive noise. However, mfcc does not app...

Gabor features have been proposed for extracting spectro-temporal modulation information, and yielding significant improvements in recognition performance. In this paper, we propose the integration of Gabor posteriors with MFCC posteriors, yielding a relative improvement of 14.3% over an MFCC Tandem system. We analyze for different types of acoustic units the complementarity between Gabor featu...

ICA which is generally used for blind source separation problem has been tested for feature extraction in Speech recognition system to replace the phoneme based approach of MFCC. Applying the Cepstral coefficients generated to ICA as preprocessing has developed a new signal processing approach. This gives much better results against MFCC and ICA separately, both for word and speaker recognition...

Abstract Aiming at the issue that recognition accuracy of traditional acoustic signal features is low for helicopter signals with wind noise in near field, a method extracting mixed MFCC+GFCC based on wavelet decomposition proposed. Firstly, three-layer and reconstruction are applied to signals; then, Mel-Frequency Cepstral Coefficients (MFCC) Gammatone-Frequency Cepstrum Coefficient (GFCC) res...

Feature extraction and selection for continuous speech recognition is a complex task. State of the art speech recognition systems use features that are derived by ignoring the Fourier transform phase. In our earlier studies we have shown the efficacy of The Modified Group Delay Feature (MODGDF) derived from the Fourier transform phase for phoneme, syllable and speaker recognition. In this paper...

The main purpose of this paper is to present how to raise the speech recognition performance in noisy environment. So far the most popularly used speech feature in speech recognition is probably the so-called MFCC. The recognition rate of speech recognition algorithm using MFCC and CDHMM is known to be very high in clean speech environment, but it deteriorates greatly in noisy environment, espe...