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20152024
most citedASSERT: Anti-Spoofing with Squeeze-Excitation and Residual neTworks

18 citations · 55 across the 20 of their papers we have counts for

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26 papers · 1 filter

eess.AS20223 cited

Defense against Adversarial Attacks on Hybrid Speech Recognition using Joint Adversarial Fine-tuning with Denoiser

Sonal Joshi, Saurabh Kataria, Yiwen Shao +4

Adversarial attacks are a threat to automatic speech recognition (ASR) systems, and it becomes imperative to propose defenses to protect them. In this paper, we perform experiments…

eess.AS2022

AdvEst: Adversarial Perturbation Estimation to Classify and Detect Adversarial Attacks against Speaker Identification

Sonal Joshi, Saurabh Kataria, Jesus Villalba +1

Adversarial attacks pose a severe security threat to the state-of-the-art speaker identification systems, thereby making it vital to propose countermeasures against them. Building…

eess.AS2022

Joint domain adaptation and speech bandwidth extension using time-domain GANs for speaker verification

Saurabh Kataria, Jesús Villalba, Laureano Moro-Velázquez +1

Speech systems developed for a particular choice of acoustic domain and sampling frequency do not translate easily to others. The usual practice is to learn domain adaptation and b…

eess.AS2021

Unsupervised Speech Segmentation and Variable Rate Representation Learning using Segmental Contrastive Predictive Coding

Saurabhchand Bhati, Jesús Villalba, Piotr Żelasko +2

Typically, unsupervised segmentation of speech into the phone and word-like units are treated as separate tasks and are often done via different methods which do not fully leverage…

eess.AS202111 cited

The JHU submission to VoxSRC-21: Track 3

Jejin Cho, Jesus Villalba, Najim Dehak

This technical report describes Johns Hopkins University speaker recognition system submitted to Voxceleb Speaker Recognition Challenge 2021 Track 3: Self-supervised speaker verifi…

eess.AS2021

Representation Learning to Classify and Detect Adversarial Attacks against Speaker and Speech Recognition Systems

Jesús Villalba, Sonal Joshi, Piotr Żelasko +1

Adversarial attacks have become a major threat for machine learning applications. There is a growing interest in studying these attacks in the audio domain, e.g, speech and speaker…