Statistical Voice Conversion Based on Wavenet

Abstract

This paper proposes a voice conversion technique based on WaveNet to directly generate target audio waveforms from acoustic features of a source speaker. In voice conversion based on statistical models, the relation between acoustic features, such as spectral parameters, extracted from source and target audio waveforms is generally modeled using statistical models, such as Gaussian mixture models and neural networks. Although modeling the relation between acoustic features is reasonable and efficient, these models are not optimized for predicting target audio waveforms because the vocoder parameters are used as intermediate representations. To overcome this problem, we developed a voice conversion method to model the relation between target audio waveforms and acoustic features extracted from source audio waveforms using WaveNet, which is a generative model for audio waveforms. The proposed model can directly generate converted audio waveforms without vocoders. Experimental results indicate that the proposed method can generate a more naturally sounding converted speech than that using a conventional DNN method.