Expressive-VC: Highly Expressive Voice Conversion with Attention Fusion of Bottleneck and Perturbation Features
Author:
Affiliation:
1. Northwestern Polytechnical University,Audio, Speech and Language Processing Group (ASLP@NPU), School of Computer Science,Xi’an,China
2. NetEase Inc.,Fuxi AI Lab,Hangzhou,China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10094559/10094560/10096057.pdf?arnumber=10096057
Reference26 articles.
1. A Speaker-Dependent WaveNet for Voice Conversion with Non-Parallel Data
2. Disentangling Content and Fine-Grained Prosody Information Via Hybrid ASR Bottleneck Features for Voice Conversion
3. Enriching Source Style Transfer in Recognition-Synthesis Based Non-Parallel Voice Conversion
4. Transferring Source Style in Non-Parallel Voice Conversion
5. Phonetic posteriorgrams for many-to-one voice conversion without parallel data training
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1. 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
2. Learning Expressive Disentangled Speech Representations with Soft Speech Units and Adversarial Style Augmentation;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30
3. Dualvc 2: Dynamic Masked Convolution for Unified Streaming and Non-Streaming Voice Conversion;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14
4. Generating Speech with Prosodic Prominence based on SSL-Visually Grounded Models;2023 26th Conference of the Oriental COCOSDA International Committee for the Co-ordination and Standardisation of Speech Databases and Assessment Techniques (O-COCOSDA);2023-12-04
5. Perturbation AUTOVC: Voice Conversion From Perturbation and Autoencoder Loss;IEEE Access;2023
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