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1 Ergebnisse
1
Mitigating Closed-Model Adversarial Examples with Bayesian ..:
, In:
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
,
Yang, Chao-Han Huck
;
Ahmed, Zeeshan
;
Gu, Yile
... - p. 6302-6306 , 2022
Link:
https://doi.org/10.1109/ICASSP43922.2022.9746230
RT T1
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
: T1
Mitigating Closed-Model Adversarial Examples with Bayesian Neural Modeling for Enhanced End-to-End Speech Recognition
UL https://suche.suub.uni-bremen.de/peid=ieee-9746230&Exemplar=1&LAN=DE A1 Yang, Chao-Han Huck A1 Ahmed, Zeeshan A1 Gu, Yile A1 Szurley, Joseph A1 Ren, Roger A1 Liu, Linda A1 Stolcke, Andreas A1 Bulyko, Ivan YR 2022 SN 2379-190X K1 Adaptation models K1 Transducers K1 Perturbation methods K1 Neural networks K1 Speech recognition K1 Speech enhancement K1 Signal processing K1 Adversarial Robustness K1 Robust Speech Recognition K1 Speech Recognition Safety K1 Sequence Modeling SP 6302 OP 6306 LK http://dx.doi.org/https://doi.org/10.1109/ICASSP43922.2022.9746230 DO https://doi.org/10.1109/ICASSP43922.2022.9746230 SF ELIB - SuUB Bremen
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