SR-HuBERT : An Efficient Pre-Trained Model for Speaker Verification
Author:
Affiliation:
1. Xiamen University,Institute of Artificial Intelligence,China
2. Xiamen University,School of Informatics,China
3. Xiamen University,School of Electronic Science and Engineering,China
Funder
National Natural Science Foundation of China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10445798/10445803/10447606.pdf?arnumber=10447606
Reference27 articles.
1. wav2vec 2.0: A framework for self-supervised learning of speech representations;Baevski;Advances in neural information processing systems,2020
2. HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units
3. Unispeech-Sat: Universal Speech Representation Learning With Speaker Aware Pre-Training
4. WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing
5. Multi-Feature Integration for Speaker Embedding Extraction
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