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Reseach Article

Cepstrum Based Voice Transformation using ANN

Published on March 2012 by J.H.Nirmal, Suparva Patnaik, Mukesh Zaveri
International Conference in Computational Intelligence
Foundation of Computer Science USA
ICCIA - Number 2
March 2012
Authors: J.H.Nirmal, Suparva Patnaik, Mukesh Zaveri
0417ebfd-e380-4459-afa6-eca6ea1f6f1e

J.H.Nirmal, Suparva Patnaik, Mukesh Zaveri . Cepstrum Based Voice Transformation using ANN. International Conference in Computational Intelligence. ICCIA, 2 (March 2012), 13-16.

@article{
author = { J.H.Nirmal, Suparva Patnaik, Mukesh Zaveri },
title = { Cepstrum Based Voice Transformation using ANN },
journal = { International Conference in Computational Intelligence },
issue_date = { March 2012 },
volume = { ICCIA },
number = { 2 },
month = { March },
year = { 2012 },
issn = 0975-8887,
pages = { 13-16 },
numpages = 4,
url = { /proceedings/iccia/number2/5099-1011/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference in Computational Intelligence
%A J.H.Nirmal
%A Suparva Patnaik
%A Mukesh Zaveri
%T Cepstrum Based Voice Transformation using ANN
%J International Conference in Computational Intelligence
%@ 0975-8887
%V ICCIA
%N 2
%P 13-16
%D 2012
%I International Journal of Computer Applications
Abstract

The basic goal of the voice conversion system to mimics the characteristics of the target speaker voice by keeping the linguistic and paralinguistic information intact. The characteristics of a speaker in speech reflect at different level such as vocal tract, excitation and prosodic parameters. This propose work based on cepstrum which represents the vocal tract and excitation parameters of the speech. This paper proposes the decomposition of the cepstrum by wavelet and mapped the source cepstrum features in to target cepstrum features using Radial basis function neural network. The results are evaluated using subjective and objective measures based on voice quality method and the listening tests prove that the proposed algorithm converts speaker individuality while maintaining high speech quality

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Index Terms

Computer Science
Information Sciences

Keywords

Wavelet transforms Voice conversion Speech cepstrum and Radial basis artificial neural network