In this paper, a new voice personality transformation algorithm which uses the vocal tract characteristics and pitch period as feature parameters is proposed. The vocal tract transfer function is divided into time-invariant and time-varying parts. Conversion rules for the time-varying part are constructed by the classified-linear transformation matrix based on soft-clustering techniques for LPC cepstrum expressed in KL (Karhunen-Loève) coefficients. An excitation signal containing prosodic information is transformed by average pitch ratio. In order to improve the naturalness, transformation on the excitation signal is separately applied to voiced and unvoiced bands to preserve the overall spectral structure. Objective tests show that the distance between the LPC cepstrum of a target speaker and that of the speech synthesized using the proposed method is reduced by about 70% compared with the distance between the target speaker's LPC cepstrum and the source speaker's. Also, subjective listening tests show that 60-70% of listeners identify the transformed speech as the target speaker's.