  In a typical voice conversion system, vocoder is commonly used for
speech-to-features analysis and features-to-speech synthesis. However, vocoder
can be a source of speech quality degradation. This paper presents a
vocoder-free voice conversion approach using WaveNet for non-parallel training
data. Instead of dealing with the intermediate features, the proposed approach
utilizes the WaveNet to map the Phonetic PosteriorGrams (PPGs) to the waveform
samples directly. In this way, we avoid the estimation errors caused by vocoder
and feature conversion. Additionally, as PPG is assumed to be speaker
independent, the proposed method also reduces the feature mismatch problem in
WaveNet vocoder based approaches. Experimental results conducted on the
CMU-ARCTIC database show that the proposed approach significantly outperforms
the baseline approaches in terms of speech quality.

Audio and Speech Processing;Sound