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Voice Conversion Using A Two-Factor Gaussian Process Latent Variable Model

Sun X., Zhang X., Cao T., Yang J., Sun J.

Przegląd Elektrotechniczny

2012

R. 88, nr 12a

318-324

This paper presents a novel strategy for voice conversion by solving style and content separation task using a two-factor Gaussian Process Latent Variable Model (GP-LVM). A generative model for speech is developed by interaction of style and content, which represent the voice individual characteristics and semantic information respectively. The interaction is captured by a GP-LVM with two latent variables, as well as a GP mapping to observation. Then, for a given collection of labelled observations, the separation task is accomplished by fitting the model with Maximum Likelihood method. Finally, voice conversion is implemented by style alternation, and the desired speech is reconstructed with the decomposed target speaker style and the source speech content using the learned model as a prior. Both objective and subjective test results show the advantage of the proposed method compared to the traditional GMM-based mapping system with limited size of training data. Furthermore, experimental results indicate that the GP-LVM with nonlinear kernel functions behaves better than that with linear ones for voice conversion due to its ability of better capturing the interaction between style and content, and rich varieties of the two factors in a training set also help to improve the conversion performance.

W artykule opisano nową strategię konwersji głosu, poprzez rozdzielenie rodzaju i treści, przy wykorzystaniu dwu-wskaźnikowej metody GPLVM (ang. Gaussian Process Latent Variable Model). Wykonane badania wskazują na lepsze działanie proponowanego algorytmu w porównaniu z tradycyjnie stosowanym systemem mapowania typu GMM przy ograniczonej ilości danych do testowania. Wykazano, że GPLVM ma lepsze właściwości w konwersji głosu z nieliniową niż liniową funkcją jądra.

Voice Conversion Based on Hybrid SVR and GMM

Song P., Jin Y., Zhao L., Zou C.

Archives of Acoustics

2012

Vol. 37, No. 2

143-149

A novel VC (voice conversion) method based on hybrid SVR (support vector regression) and GMM (Gaussian mixture model) is presented in the paper, the mapping abilities of SVR and GMM are exploited to map the spectral features of the source speaker to those of target ones. A new strategy of F0 transfor- mation is also presented, the F0s are modeled with spectral features in a joint GMM and predicted from the converted spectral features using the SVR method. Subjective and objective tests are carried out to evaluate the VC performance; experimental results show that the converted speech using the proposed method can obtain a better quality than that using the state-of-the-art GMM method. Meanwhile, a VC method based on non-parallel data is also proposed, the speaker-specific information is investigated us- ing the SVR method and preliminary subjective experiments demonstrate that the proposed method is feasible when a parallel corpus is not available.

Analysis of State-Space Model based Voice Conversion

Sun J., Zhang X.

Przegląd Elektrotechniczny

2011

R. 87, nr 10

373-376

A new State-Space Model (SSM) based voice conversion method has been proposed recently which outperforms the traditional Gaussian Mixture Model (GMM) method. Although the implementation process of the new method has been elaborated, the theoretical essence of this method has not been analysed clearly. In this paper an exhaustive analysis of the SSM based method is given theoretically and experimentally. Through these analysis, much simpler equivalence form and performance upper bound of the new method are obtained. Finally possible improvements are discussed.

Przedstawiono teoretyczna i eksperymentalną analizę nowego algorytm SSM przetwarzania sygnału mowy.