LE-SSL-MOS: Self-Supervised Learning MOS Prediction with Listener Enhancement
Author:
Affiliation:
1. Hithink RoyalFlush AI Research Institute,Zhejiang,China
2. Kyoto University,Kyoto,Japan
3. National Institute of Information and Communications Technology (NICT),Kyoto,Japan
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10388490/10389614/10389788.pdf?arnumber=10389788
Reference25 articles.
1. MOSNet: Deep Learning-Based Objective Assessment for Voice Conversion
2. MBNET: MOS Prediction for Synthesized Speech with Mean-Bias Network
3. LDNet: Unified Listener Dependent Modeling in MOS Prediction for Synthetic Speech
4. Representation learning with contrastive predictive coding;Oord
5. Generalization Ability of MOS Prediction Networks
Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. SALF-MOS: Speaker Agnostic Latent Features Downsampled for MOS Prediction;2024 International Conference on Signal Processing and Communications (SPCOM);2024-07-01
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