# A Vocoder-free WaveNet Voice Conversion with Non-Parallel Data

arXiv: https://arxiv.org/abs/1902.03705 · HTML: https://mlsanity.com/paper/1902.03705/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Feb 2019.

## Built on by (most cited first, 2 of 2)

- [Multilingual Prosody Transfer: Comparing Supervised & Transfer Learning](https://mlsanity.com/paper/2406.00022/) (2024)
- [MoEVC: A Mixture-of-experts Voice Conversion System with Sparse Gating Mechanism for Accelerating Online Computation](https://mlsanity.com/paper/1912.11984/) (2019)

## Abstract (arXiv)

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.