Voice Conversion

EAD-VC: Enhancing Speech Auto-Disentanglement for Voice Conversion with IFUB Estimator and Joint Text-Guided Consistent Learning
Using unsupervised learning to disentangle speech into content, rhythm, pitch, and timbre for voice conversion has become a hot …
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EAD-VC: Enhancing Speech Auto-Disentanglement for Voice Conversion with IFUB Estimator and Joint Text-Guided Consistent Learning
Learning Expressive Disentangled Speech Representations with Soft Speech Units and Adversarial Style Augmentation
Voice conversion is the task to transform voice characteristics of source speech while preserving content information. Nowadays, …
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Learning Expressive Disentangled Speech Representations with Soft Speech Units and Adversarial Style Augmentation
MAIN-VC: Lightweight Speech Representation Disentanglement for One-Shot Voice Conversion
One-shot voice conversion aims to change the timbre of any source speech to match that of the unseen target speaker with only one …
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MAIN-VC: Lightweight Speech Representation Disentanglement for One-Shot Voice Conversion
Learning Disentangled Speech Representations with Contrastive Learning and Time-Invariant Retrieval
Voice conversion refers to transferring speaker identity with well-preserved content. Better disentanglement of speech representations …
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Learning Disentangled Speech Representations with Contrastive Learning and Time-Invariant Retrieval
PMVC: Data Augmentation-Based Prosody Modeling for Expressive Voice Conversion
Voice conversion as the style transfer task applied to speech, refers to converting one person’s speech into a new speech that …
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PMVC: Data Augmentation-Based Prosody Modeling for Expressive Voice Conversion
CLN-VC: Text-Free Voice Conversion Based on Fine-Grained Style Control and Contrastive Learning with Negative Samples Augmentation
Better disentanglement of speech representation is essential to improve the quality of voice conversion. Recently contrastive learning …
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CLN-VC: Text-Free Voice Conversion Based on Fine-Grained Style Control and Contrastive Learning with Negative Samples Augmentation
Voice Conversion with Denoising Diffusion Probabilistic GAN Models
Voice conversion is a method that allows for the transformation of speaking style while maintaining the integrity of linguistic …
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Voice Conversion with Denoising Diffusion Probabilistic GAN Models
Learning Speech Representations with Flexible Hidden Feature Dimensions
Non-parallel many-to-many voice conversion is a kind of style transfer task in speech. Recently, AutoVC has been applied in this field …
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Learning Speech Representations with Flexible Hidden Feature Dimensions
VQ-CL: Learning Disentangled Speech Representations with Contrastive Learning and Vector Quantization
Voice Conversion(VC) refers to converting the voice char- acteristics of audio to another one as it is said by other people. Recently, …
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VQ-CL: Learning Disentangled Speech Representations with Contrastive Learning and Vector Quantization
Boosting Star-GANs for Voice Conversion with Contrastive Discriminator
Nonparallel multi-domain voice conversion methods such as the StarGAN-VCs have been widely applied in many scenarios. However, the …
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Boosting Star-GANs for Voice Conversion with Contrastive Discriminator