Avqvc: One-Shot Voice Conversion By Vector Quantization With Applying Contrastive Learning
Author:
Affiliation:
1. Ping An Technology (Shenzhen) Co., Ltd
Funder
Research and Development
National Key Research and Development Program of China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9745891/9746004/09746369.pdf?arnumber=9746369
Reference23 articles.
1. Unsupervised Speech Representation Learning Using WaveNet Autoencoders
2. One-Shot Voice Conversion by Vector Quantization
3. VQVC+: One-Shot Voice Conversion by Vector Quantization and U-Net Architecture
4. Speech2Vec: A Sequence-to-Sequence Framework for Learning Word Embeddings from Speech
5. Representation learning with contrastive predictive coding;van den oord,2018
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2. EAD-VC: Enhancing Speech Auto-Disentanglement for Voice Conversion with IFUB Estimator and Joint Text-Guided Consistent Learning;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30
3. Learning Expressive Disentangled Speech Representations with Soft Speech Units and Adversarial Style Augmentation;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30
4. VQStarGAN: An Unseen-to-Unseen, Zero-Shot, Non-Parallel, Unsupervised Voice Conversion;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30
5. MAIN-VC: Lightweight Speech Representation Disentanglement for One-shot Voice Conversion;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30
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