In this paper we present a method to derive Mel-frequency cepstral coefficients directly from the power spectrum of a speech signal. We show that omitting the filterbank in signal analysis does not affect the word error rate. The presented approach simplifies the speech recognizer’s front end by merging subsequent signal analysis steps into a single one. It avoids possible interpolation and dis...

Most speech recognition systems are based on melfrequency cepstral coefficients and their firstand secondorder derivatives. The derivatives are normally approximated by fitting a linear regression line to a fixed-length segment of consecutive frames. The time resolution and smoothness of the estimated derivative depends on the length of the segment. We present an approach to improve the represe...

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This paper presents an experimental evaluation of different features and channel compensation techniques for robust speaker identification. The goal is to keep all processing and classification steps constant and to vary only the features and compensations used to allow a controlled comparison. A general, maximum-likelihood classifier based on Gaussian mixture densities is used as the classifie...

The generalized cepstral analysis method is viewed as a unified approach to the cepstral method and the linear prediction method, in which the model spectrum varies continuously from all-pole to cepstral according to the value of a parameter γ. Since the human ear has high resolution at low frequencies, introducing similar characteristics to the model spectrum, we can represent speech spectrum ...

The goal is to improve recognition rate by optimisation of Mel Frequency Cepstral Coe cients (MFCCs): modi cations concern the time-frequency representations used to estimate these coe cients. There are many ways to obtain a spectrum out of a signal which di er in the method itself (Fourier, Wavelets,...), and in the normalisation. We show here that we can obtain noise resistant cepstral coe ci...

This paper addresses the performance of various statistical data fusion techniques for combining the complementary score information in speaker verification. The complementary verification scores are based on the static and delta cepstral features. Both LPCC (Linear prediction-based cepstral coefficients) and MFCC (mel-frequency cepstral coefficients) are considered in the study. The experiment...

The number of channels is one the important criteria in regard to digital audio quality. Generally, stereo with two can provide better perceptual quality than mono audio. To seek illegal commercial benefit, might convert a system fake Identifying stereo-faking lesser-investigated forensic issue. In this paper, faking corpus first presented, which created using Haas effect technique. Two identif...

Mel-frequency cepstral coefficients are widely used as the feature for speech recognition. In MFCC extraction process, the spectrum, obtained by Fourier transform of input speech signal is divided by mel-frequency bands, and each ban energy is extracted for the each frequency band. The coefficients are extracted by the discrete cosine transform of the obtained band energy. In this paper, we cal...

Processing of the speech signal in the autocorrelation domain in the context of robust feature extraction is based on the following two properties: 1) pole preserving property (the poles of a given (original) signal are preserved in its autocorrelation function), and 2) noise separation property (the autocorrelation function of a noise signal is confined to lower lags, while the speech signal c...