FastVC: Fast Voice Conversion with non-parallel data

Abstract

This paper introduces FastVC, an end-to-end model for fast Voice Conversion (VC). The proposed model can convert speech of arbitrary length from multiple source speakers to multiple target speakers. FastVC is based on a conditional AutoEncoder (AE) trained on non-parallel data and requires no annotations at all. This model's latent representation is shown to be speaker-independent and similar to phonemes, which is a desirable feature for VC systems. While the current VC systems primarily focus on achieving the highest overall speech quality, this paper tries to balance the development concerning resources needed to run the systems. Despite the simple structure of the proposed model, it outperforms the VC Challenge 2020 baselines on the cross-lingual task in terms of naturalness.

References in corpus (11)

• Adam: A Method for Stochastic Optimization
• WaveNet: A Generative Model for Raw Audio
• Neural Discrete Representation Learning
• MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis
• AUTOVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss
• F0-consistent many-to-many non-parallel voice conversion via conditional autoencoder
• StarGAN-VC: Non-parallel many-to-many voice conversion with star generative adversarial networks
• Generative Adversarial Network Training is a Continual Learning Problem
• An Overview of Voice Conversion and its Challenges: From Statistical Modeling to Deep Learning
• Non-Parallel Voice Conversion with Cyclic Variational Autoencoder
• Multi-target Voice Conversion without Parallel Data by Adversarially Learning Disentangled Audio Representations