Statistical Voice Conversion with Quasi-Periodic WaveNet Vocoder
Yi-Chiao Wu, Patrick Lumban Tobing, Tomoki Hayashi, Kazuhiro, Kobayashi, Tomoki Toda

TL;DR
This paper introduces a quasi-periodic WaveNet (QPNet) vocoder that improves robustness and pitch controllability in statistical voice conversion, outperforming traditional WaveNet vocoders with smaller network sizes.
Contribution
The paper proposes a QPNet vocoder with pitch-dependent dilated convolution, enhancing robustness and pitch control in voice conversion compared to standard WaveNet vocoders.
Findings
QPNet outperforms the same-size WaveNet vocoder in quality.
QPNet maintains comparable speech quality to larger WaveNet vocoders.
The method improves pitch controllability and robustness in voice conversion.
Abstract
In this paper, we investigate the effectiveness of a quasi-periodic WaveNet (QPNet) vocoder combined with a statistical spectral conversion technique for a voice conversion task. The WaveNet (WN) vocoder has been applied as the waveform generation module in many different voice conversion frameworks and achieves significant improvement over conventional vocoders. However, because of the fixed dilated convolution and generic network architecture, the WN vocoder lacks robustness against unseen input features and often requires a huge network size to achieve acceptable speech quality. Such limitations usually lead to performance degradation in the voice conversion task. To overcome this problem, the QPNet vocoder is applied, which includes a pitch-dependent dilated convolution component to enhance the pitch controllability and attain a more compact network than the WN vocoder. In the…
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Taxonomy
TopicsSpeech Recognition and Synthesis · Speech and Audio Processing · Advanced Data Compression Techniques
