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Voice conversion (VC) is a technique aiming to mapping the individuality of a source speaker to that of a target speaker, wherein Gaussian mixture model (GMM) based methods are evidently prevalent. Despite their wide use, two major problems remains to be resolved, i.e., over-smoothing and over-fitting. The latter one arises naturally when the structure of model is too complicated given limited amount...
One of the most recent models for voice conversion is the classical LPC analysis-synthesis model combined with GMM, which aims to separate information from excitation and vocal tract and to learn the transformation rules with statistical methods. However, it does not work well as it is supposed to be due to the inaccuracy of the extracted feature information as well as the overly-smoothed spectral...
This paper presents a novel voice morphing system which reproduces high quality speech while maintaining the majority of the target characteristics. Bi-GMM is named for using GMM technique to estimate mapping functions as well as a codebook generated by GMM either. Compared with the traditional GMM technique, a maximum likelihood estimation framework combined with codebook compensation technique is...
A novel algorithm for voice conversion is proposed in this paper. The mapping function of spectral vectors of the source and target speakers is calculated by the canonical correlation analysis (CCA) estimation based on Gaussian mixture models. Since the spectral envelope feature remains a majority of second order statistical information contained in speech after linear prediction (LPC) analysis, the...
A novel gender classification system has been proposed based on Gaussian mixture models, which apply the combined parameters of pitch and 10th order relative spectral perceptual linear predictive coefficients to model the characteristics of male and female speech. The performances of gender classification system have been evaluated on the conditions of clean speech, noisy speech and multi-language...
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