Author(s): Suparva Patnaik | Mukesh Zaveri
Journal: International Journal of Computer Applications
ISSN 0975-8887
Volume: iccia;
Issue: 2;
Date: 2012;
Original page
Keywords: Wavelet transforms Voice conversion | Speech cepstrum | and Radial basis artificial neural network
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
Journal: International Journal of Computer Applications
ISSN 0975-8887
Volume: iccia;
Issue: 2;
Date: 2012;
Original page
Keywords: Wavelet transforms Voice conversion | Speech cepstrum | and Radial basis artificial neural network
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
