Enhancing Zero-Shot Many to Many Voice Conversion via Self-Attention VAE with Structurally Regularized Layers
Author:
Affiliation:
1. University of California,Department of Mathematics,Irvine,CA,USA
2. Tencent AI Lab Tencent at Bellevue,Seattle,WA,USA
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10123435/10123441/10123589.pdf?arnumber=10123589
Reference42 articles.
1. Parrotron: An End-to-End Speech-to-Speech Conversion Model and its Applications to Hearing-Impaired Speech and Speech Separation
2. Voice Transformer Network: Sequence-to-Sequence Voice Conversion Using Transformer with Text-to-Speech Pretraining
3. Map-based adaptation for speech conversion using adaptation data selection and non-parallel training
4. One-to-many voice conversion based on tensor representation of speaker space
5. A KL Divergence and DNN-Based Approach to Voice Conversion without Parallel Training Sentences
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1. Reimagining speech: a scoping review of deep learning-based methods for non-parallel voice conversion;Frontiers in Signal Processing;2024-08-16
2. A Study on Combining Non-Parallel and Parallel Methodologies for Mandarin-English Cross-Lingual Voice Conversion;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14
3. Improving Voice Style Conversion via Self-attention VAE with Feature Disentanglement;Communications in Computer and Information Science;2024
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