90 citations · 178 across the 45 of their papers we have counts for
6 papers · 1 filter
Time-domain speech super-resolution with GAN based modeling for telephony speaker verification
Saurabh Kataria, Jesús Villalba, Laureano Moro-Velázquez +2
Automatic Speaker Verification (ASV) technology has become commonplace in virtual assistants. However, its performance suffers when there is a mismatch between the train and test d…
Non-Contrastive Self-supervised Learning for Utterance-Level Information Extraction from Speech
Jaejin Cho, Jes'us Villalba, Laureano Moro-Velazquez +1
In recent studies, self-supervised pre-trained models tend to outperform supervised pre-trained models in transfer learning. In particular, self-supervised learning (SSL) of uttera…
Non-Contrastive Self-Supervised Learning of Utterance-Level Speech Representations
Jaejin Cho, Raghavendra Pappagari, Piotr Żelasko +3
Considering the abundance of unlabeled speech data and the high labeling costs, unsupervised learning methods can be essential for better system development. One of the most succes…
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…
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…
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…