Voice Conversion Based on Maximum-Likelihood Estimation of Spectral Parameter Trajectory

In this paper, we describe a novel spectral conversion method for voice conversion (VC). A Gaussian mixture model (GMM) of the joint probability density of source and target features is employed for performing spectral conversion between speakers. The conventional method converts spectral parameters...

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Veröffentlicht in:	IEEE transactions on audio, speech, and language processing speech, and language processing, 2007-11, Vol.15 (8), p.2222-2235
Hauptverfasser:	Toda, T., Black, A.W., Tokuda, K.
Format:	Artikel
Sprache:	eng
Schlagworte:	Applied sciences Conversion Density Dynamic feature Exact sciences and technology Frames Gaussian mixture model (GMM) global variance Information, signal and communications theory Loudspeakers Mathematical models Maximum likelihood estimation maximum-likelihood estimation (MLE) Mean square error methods Mean square errors Natural languages Parameter estimation Signal processing Spectra Speech Speech enhancement Speech processing Statistics Studies Telecommunications and information theory Training data Trajectories Virtual colonoscopy Voice voice conversion (VC)
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