ACE-VC: Adaptive and Controllable Voice Conversion Using Explicitly Disentangled Self-Supervised Speech Representations
Author:
Affiliation:
1. University of California,San Diego,CA,USA
2. NVIDIA Corporation,Santa Clara,CA,USA
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10094559/10094560/10094850.pdf?arnumber=10094850
Reference26 articles.
1. wav2vec 2.0: A framework for self-supervised learning of speech representations;baevski;NeurIPS,2020
2. S3PRL-VC: Open-Source Voice Conversion Framework with Self-Supervised Speech Representations
3. Multitask voice activated framework using self-supervised learning;hussain;ICASSP,2022
4. Conformer: Convolution-augmented Transformer for Speech Recognition
5. Fragmentvc: Any-To-Any Voice Conversion by End-To-End Extracting and Fusing Fine-Grained Voice Fragments with Attention
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2. 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
3. SEF-VC: Speaker Embedding Free Zero-Shot Voice Conversion with Cross Attention;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14
4. Using Joint Training Speaker Encoder With Consistency Loss to Achieve Cross-Lingual Voice Conversion and Expressive Voice Conversion;2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU);2023-12-16
5. SLMGAN: Exploiting Speech Language Model Representations for Unsupervised Zero-Shot Voice Conversion in GANs;2023 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA);2023-10-22
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