AGAIN-VC: A One-shot Voice Conversion using Activation Guidance and Adaptive Instance Normalization
arXiv:2011.00316
Abstract
Recently, voice conversion (VC) has been widely studied. Many VC systems use disentangle-based learning techniques to separate the speaker and the linguistic content information from a speech signal. Subsequently, they convert the voice by changing the speaker information to that of the target speaker. To prevent the speaker information from leaking into the content embeddings, previous works either reduce the dimension or quantize the content embedding as a strong information bottleneck. These mechanisms somehow hurt the synthesis quality. In this work, we propose AGAIN-VC, an innovative VC system using Activation Guidance and Adaptive Instance Normalization. AGAIN-VC is an auto-encoder-based model, comprising of a single encoder and a decoder. With a proper activation as an information bottleneck on content embeddings, the trade-off between the synthesis quality and the speaker similarity of the converted speech is improved drastically. This one-shot VC system obtains the best performance regardless of the subjective or objective evaluations.
arXiv commentSubmitted to ICASSP 2021
References in corpus (7)
- Auto-Encoding Variational Bayes
- Neural Discrete Representation Learning
- MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis
- Parallel-Data-Free Voice Conversion Using Cycle-Consistent Adversarial Networks
- Voice Conversion from Unaligned Corpora using Variational Autoencoding Wasserstein Generative Adversarial Networks
- F0-consistent many-to-many non-parallel voice conversion via conditional autoencoder
- Unsupervised Speech Decomposition via Triple Information Bottleneck