Target Speech Extraction with Pre-Trained Self-Supervised Learning Models
Author:
Affiliation:
1. Brno University of Technology,Faculty of Information Technology, Speech@FIT,Czechia
2. NTT Corporation,Japan
Funder
National Science Foundation
Ministry of Education
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10445798/10445803/10448315.pdf?arnumber=10448315
Reference27 articles.
1. HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units
2. wav2vec 2.0: A framework for self-supervised learning of speech representations;Baevski;Advances in neural information processing systems,2020
3. Joint Encoder-Decoder Self-Supervised Pre-training for ASR
4. Fine-Tuning Wav2Vec2 for Speaker Recognition
5. Large-Scale Self-Supervised Speech Representation Learning for Automatic Speaker Verification
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1. Gated Cross-Attention for Universal Speaker Extraction: Toward Real-World Applications;Electronics;2024-05-24
2. Probing Self-Supervised Learning Models With Target Speech Extraction;2024 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW);2024-04-14
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