This paper presents the NU-NAIST voice conversion (VC) system for the Voice Conversion Challenge 2016 (VCC 2016) developed by a joint team of Nagoya University and Nara Institute of Science and Technology. Statistical VC based on a Gaussian mixture model makes it possible to convert speaker identity of a source speaker' voice into that of a target speaker by converting several speech parameters. However, various factors such as parameterization errors and over-smoothing effects usually cause speech quality degradation of the converted voice. To address this issue, we have proposed a direct waveform modification technique based on spectral differential filtering and have successfully applied it to singing voice conversion where excitation features are not necessary converted. In this paper, we propose a method to apply this technique to a standard voice conversion task where excitation feature conversion is needed. The result of VCC 2016 demonstrates that the NU-NAIST VC system developed by the proposed method yields the best conversion accuracy for speaker identity (more than 70% of the correct rate) and quite high naturalness score (more than 3 of the mean opinion score). This paper presents detail descriptions of the NU-NAIST VC system and additional results of its performance evaluation.
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