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Journal ArticleDOI

A fast algorithm for finding the adaptive component weighted cepstrum for speaker recognition

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TLDR
This new method, which avoids root finding, reduces the computer time significantly and imposes negligible overhead when compared with the approach of finding the LP cepstrum.
Abstract
In speaker recognition systems, the adaptive component weighted (ACW) cepstrum has been shown to be more robust than the conventional linear predictive (LP) cepstrum. The ACW cepstrum is derived from a pole-zero transfer function whose denominator is the pth-order LP polynomial A(z). The numerator is a (p-1)th-order polynomial that is up to now found as follows. The roots of A(z) are computed, and the corresponding residues obtained by a partial fraction expansion of 1/A(z) are set to unity. Therefore, the numerator is the sum of all the (p-1)th-order cofactors of A(z). We show that the numerator polynomial is merely the derivative of the denominator polynomial A(z). This greatly speeds up the computation of the numerator polynomial coefficients since it involves a simple scaling of the denominator polynomial coefficients. Root finding is completely eliminated. Since the denominator is guaranteed to be minimum phase and the numerator can be proven to be minimum phase, two separate recursions involving the polynomial coefficients establishes the ACW cepstrum. This new method, which avoids root finding, reduces the computer time significantly and imposes negligible overhead when compared with the approach of finding the LP cepstrum.

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Citations
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Journal ArticleDOI

Robust speaker recognition: a feature-based approach

TL;DR: Linear predictive (LP) analysis, the first step of feature extraction, is discussed, and various robust cepstral features derived from LP coefficients are described, including the afJine transform, which is a feature transformation approach that integrates mismatch to simultaneously combat both channel and noise distortion.
Journal ArticleDOI

Speaker identification based on the use of robust cepstral features obtained from pole-zero transfer functions

TL;DR: This work proposes four additional new cepstral features that show less variation when speech is corrupted by convolutional noise (channel) and/or additive noise and proposes an alternative way of doing adaptive component weighting called the ACW2 cepstrum.
Journal Article

Nearest Neighbourhood Classifiers in a Bimodal Biometric Verification System Fusion Decision Scheme

TL;DR: K-Nearest Neighbourhood (k-NN) based classifiers are adopted in the decision fusionmodule for the face and speech experts and compared with other classification approaches such as sum rule, voting techniques and Multilayer Perceptron on a bimodal database.
Proceedings ArticleDOI

Speech based emotion recognition using spectral feature extraction and an ensemble of kNN classifiers

TL;DR: Results show that the maximum gain in performance is achieved by using two kNNs as opposed to using a single kNN, and fusion is implicitly accomplished by ensemble classification.
Proceedings ArticleDOI

Computation of the One-Dimensional Unwrapped Phase

TL;DR: Two composite algorithms are proposed that build upon the existing ones based on recent advances in polynomial factoring for computing the unwrapped phase of the discrete-time Fourier transform of a one-dimensional finite-length signal.
References
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Journal ArticleDOI

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Journal ArticleDOI

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TL;DR: A discussion of inherent performance limitations, along with a review of the performance achieved by listening, visual examination of spectrograms, and automatic computer techniques, attempts to provide a perspective with which to evaluate the potential of speaker recognition and productive directions for research into and application of speaker Recognition technology.