Voice Conversion

Speech Representation Disentanglement with Adversarial Mutual Information Learning for One-shot Voice Conversion
One-shot voice conversion (VC) with only a single target-speaker speech for reference has become a new research direction. Existing …
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Speech Representation Disentanglement with Adversarial Mutual Information Learning for One-shot Voice Conversion
AVQVC: One-Shot Voice Conversion By Vector Quantization With Applying Contrastive Learning
Voice Conversion(VC) refers to changing the timbre of a speech while retaining the discourse content. Recently, many works have focused …
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AVQVC: One-Shot Voice Conversion By Vector Quantization With Applying Contrastive Learning
DRVC: A Framework of Any-to-Any Voice Conversion with Self-Supervised Learning
Any-to-any voice conversion problem aims to convert voices for source and target speakers, which are out of the training data. Previous …
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DRVC: A Framework of Any-to-Any Voice Conversion with Self-Supervised Learning
CycleGEAN: Cycle Generative Enhanced Adversarial Network for Voice Conversion
Cycle Generative Adversarial Network (CycleGAN) for voice conversion (VC) task only used discriminators to identify whether the input …
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CycleGEAN: Cycle Generative Enhanced Adversarial Network for Voice Conversion
Reconstructing Dual Learning for Neural Voice Conversion Using Relatively Few Samples
This paper introduces a dual learning system for neural voice conversion (DualVC) using relatively few samples based on the symmetry of …
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Reconstructing Dual Learning for Neural Voice Conversion Using Relatively Few Samples
TGAVC: Improving Autoencoder Voice Conversion with Text-Guided and Adversarial Training
Non-parallel many-to-many voice conversion remains an interesting but challenging speech processing task. Recently, AutoVC, a …
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TGAVC: Improving Autoencoder Voice Conversion with Text-Guided and Adversarial Training