This paper proposes a structured Gaussian mixture model with constraint conditions (C-SGMM) for non-parallel corpora voice conversion.A small number of voice signals with the same syllables from the source and target non-parallel corpus are extracted as constraint conditions

then the correspondence between acoustic features of source and target corpus formed by these syllables are applied in the process of statistical acoustic model training.The constraint conditions are used to restrict

the cluster centers of

K

-means clustering process

and they are also used in EM algorithm to adjust the voice frame's posterior probability belonging to a Gaussian distribution component for model training.Then Gaussian distributions in source and target structured Gaussian mixture models are aligned using acoustic universal structure principle and the conversion function can be derived.Results of both subjective and objective experiments indicate that the conversion performance obtained by the proposed method are advanced to that of the traditional structured method in cepstrum distortion

target tendency and speech quality aspects.The average cepstrum distortion of converted speech is only 0.52

the speaker recognition rate of the converted speech reaches 95.25%

and the performance closer to the conventional parallel corpora GMM based method is achieved.