Abstrtct. A new method for representation of speech spectra based on a pole-zero decomposition technique is proposed in this paper. In this method the parameters of a pole-zero model for the smoothed short-time spectrum of speech are determined by adopting a cepstral matching criterion. The cepstral coefficients of the impulse response of the model are equal to the cepstral coefficients of the ...

FastICA is a kind of independent component analysis (ICA), which is robust and high performance algorithm, it can strongly remove signal correlation and ensure each signal to be independence. Through perceptual test, improving that RASTA is an idea which can denoise effectively. First, we remove signal correlation through FastICA algorithm, then we use RASTA filter to filtering the ceptral coef...

Due to the increasing use of fusion in speaker recognition systems, one trend of current research activity focuses on new features that capture complementary information to the MFCC (Mel-frequency cepstral coefficients) for improving speaker recognition performance. The goal of this work is to combine (or fuse) amplitude and phase-based features to improve speaker verification performance. Base...

In the past few years, a great deal of research has been directed toward finding acoustic features that are effective for automatic speech recognition. Until recently, most of the speech recognizers used about 12 cepstral coefficients derived through the linear prediction analysis as recognition features [ 11. In [2,3], Furui investigated the use of temporal derivatives of cepstral coefficients...

Automatic Speaker Recognition (ASR) is an economic tool for voice biometrics because of availability of low cost and powerful processors. For an ASR system to be successful in practical environments, it must have high mimic resistance, i.e., the system should not be defeated by determined mimics which may be either identical twins or professional mimics. In this paper, we demonstrate the effect...

This paper describes a speaker identification system that uses complementary acoustic features derived from the vocal source excitation and the vocal tract system. Conventional speaker recognition systems typically adopt the cepstral coefficients, e.g., Mel-frequency cepstral coefficients (MFCC) and linear predictive cepstral coefficients (LPCC), as the representative features. The cepstral fea...

Enhancing the performance of emotional speaker recognition process has witnessed an increasing interest in the last years. This paper highlights a methodology for speaker recognition under different emotional states based on the multiclass Support Vector Machine (SVM) classifier. We compare two feature extraction methods which are used to represent emotional speech utterances in order to obtain...

This paper presents an effective method for improving the performance of a speaker identification system. Based on the multiresolution property of the wavelet transform, the input speech signal is decomposed into various frequency bands in order not to spread noise distortions over the entire feature space. To capture the characteristics of the vocal tract, the linear predictive cepstral coeffi...