Self-Supervised Fine-Tuning of Automatic Speech Recognition Systems against Signal Processing Attacks-Reference-Cited by-同舟云学术

Self-Supervised Fine-Tuning of Automatic Speech Recognition Systems against Signal Processing Attacks

Published:2024-07 Issue: Volume:29 Page:1272-1286
ISSN:
Container-title:Proceedings of the 19th ACM Asia Conference on Computer and Communications Security
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Author:

Jayawardena Oshan¹^ORCID,Caldera Dilmi¹^ORCID,Jayawardena Sandani¹^ORCID,Sandeepa Avishka¹^ORCID,Bindschaedler Vincent²^ORCID,Charles Subodha¹^ORCID

Affiliation:

1. Department of Electronic and Telecommunication Engineering, University of Moratuwa, Moratuwa, Western Province, Sri Lanka

2. CISE, University of Florida, Gainesville, Florida, United States of America

Funder

U.S. National Science Foundation

Publisher

ACM

Reference37 articles.

1. 2023. Conformer-2: a state-of-the-art speech recognition model trained on 1.1M hours of data. https://www.assemblyai.com/blog/conformer-2/. (Accessed on 08/09/2023).

2. Sajjad Abdoli, Luiz G Hafemann, Jerome Rony, Ismail Ben Ayed, Patrick Cardinal, and Alessandro L Koerich. 2019. Universal adversarial audio perturbations. arXiv preprint arXiv:1908.03173 (2019).

3. Hadi Abdullah, Washington Garcia, Christian Peeters, Patrick Traynor, Kevin RB Butler, and Joseph Wilson. 2019. Practical hidden voice attacks against speech and speaker recognition systems. arXiv preprint arXiv:1904.05734 (2019).

4. Hadi Abdullah, Aditya Karlekar, Vincent Bindschaedler, and Patrick Traynor. 2021. Demystifying limited adversarial transferability in automatic speech recognition systems. In International Conference on Learning Representations (ICLR).

5. Hadi Abdullah, Aditya Karlekar, Saurabh Prasad, Muhammad Sajidur Rahman, Logan Blue, Luke A. Bauer, Vincent Bindschaedler, and Patrick Traynor. 2022. Attacks as Defenses: Designing Robust Audio CAPTCHAs Using Attacks on Automatic Speech Recognition Systems. arXiv preprint arXiv:2203.05408 (2022).