This paper describes a novel approach to voice conversion using both a joint density model and a speaker model. In voice conversion studies, approaches based on Gaussian Mixture Model (GMM) with probabilistic densities of joint vectors of a source and a target speakers are widely used to estimate a transformation. However, for sufficient quality, they require a parallel corpus which contains plenty of utterances with the same linguistic content spoken by both the speakers. In addition, the joint density GMM methods often suffer from over-training effects when the amount of training data is small. To compensate for these problems, we propose a novel approach to integrate the speaker GMM of the target with the joint density model using probabilistic formulation. The proposed method trains the joint density model with a few parallel utterances, and the speaker model with non-parallel data of the target, independently. It eases the burden on the source speaker. Experiments demonstrate the effectiveness of the proposed method, especially when the amount of the parallel corpus is small.