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Title of Journal: Neural Comput Applic

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Abbravation: Neural Computing and Applications

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Springer London

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DOI

10.1007/bf00555225

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1433-3058

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Enhancement of a textindependent speaker verifica

Authors: Kerlos Atia Abdalmalak Ascensión GallardoAntolín
Publish Date: 2016/07/16
Volume: 29, Issue: 3, Pages: 637-651
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Abstract

Speaker verification SV systems involve mainly two individual stages feature extraction and classification In this paper we explore these two modules with the aim of improving the performance of a speaker verification system under noisy conditions On the one hand the choice of the most appropriate acoustic features is a crucial factor for performing robust speaker verification The acoustic parameters used in the proposed system are Mel Frequency Cepstral Coefficients their first and second derivatives Deltas and Delta–Deltas Bark Frequency Cepstral Coefficients Perceptual Linear Predictive and Relative Spectral Transform Perceptual Linear Predictive In this paper a complete comparison of different combinations of the previous features is discussed On the other hand the major weakness of a conventional support vector machine SVM classifier is the use of generic traditional kernel functions to compute the distances among data points However the kernel function of an SVM has great influence on its performance In this work we propose the combination of two SVMbased classifiers with different kernel functions linear kernel and Gaussian radial basis function kernel with a logistic regression classifier The combination is carried out by means of a parallel structure approach in which different voting rules to take the final decision are considered Results show that significant improvement in the performance of the SV system is achieved by using the combined features with the combined classifiers either with clean speech or in the presence of noise Finally to enhance the system more in noisy environments the inclusion of the multiband noise removal technique as a preprocessing stage is proposedThe authors want to thank Erasmus Mundus “GreenIT” program for its grant for providing the funding for this work This work has also been partially supported by the Spanish Government Grant TEC201453390P and by the Regional Government of Madrid S2013/ICE2845CASICAM–CM project

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