Improving Generalizability of Distilled Self-Supervised Speech Processing Models Under Distorted Settings
Author:
Affiliation:
1. National Taiwan University
2. Nanyang Technological University
3. Google Brain
Funder
Microsoft
Google
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10022052/10022330/10022474.pdf?arnumber=10022474
Reference32 articles.
1. Improving Noise Robustness of Contrastive Speech Representation Learning with Speech Reconstruction
2. Noise adaptive speech enhancement using domain adversarial training;liao;ArXiv Preprint,2018
3. Bootstrap your own latent-a new approach to self-supervised learning;grill;Advances in neural information processing systems,2020
4. A Noise-Robust Self-Supervised Pre-Training Model Based Speech Representation Learning for Automatic Speech Recognition
5. fairseq: A Fast, Extensible Toolkit for Sequence Modeling
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1. Noise Robust Distillation of Self-Supervised Speech Models via Correlation Metrics;2024 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW);2024-04-14
2. VIC-KD: Variance-Invariance-Covariance Knowledge Distillation to Make Keyword Spotting More Robust Against Adversarial Attacks;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14
3. Zero Resource Code-Switched Speech Benchmark Using Speech Utterance Pairs for Multiple Spoken Languages;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14
4. Environment-Aware Knowledge Distillation for Improved Resource-Constrained Edge Speech Recognition;Applied Sciences;2023-11-22
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