Search Results for author:
Found 8 papers, 3 papers with code
SpeechGuard: Exploring the Adversarial Robustness of Multimodal Large Language Models
Despite safety guardrails, experiments on jailbreaking demonstrate the vulnerability of SLMs to adversarial perturbations and transfer attacks, with average attack success rates of 90% and 10% respectively when evaluated on a dataset of carefully designed harmful questions spanning 12 different toxic categories.
VoxWatch: An open-set speaker recognition benchmark on VoxCeleb
Prior studies on this problem are sparse, and lack a common benchmark for systematic evaluations.
Mel Frequency Spectral Domain Defenses against Adversarial Attacks on Speech Recognition Systems
A variety of recent works have looked into defenses for deep neural networks against adversarial attacks particularly within the image processing domain.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
To train or not to train adversarially: A study of bias mitigation strategies for speaker recognition
In this paper, we systematically evaluate the biases present in speaker recognition systems with respect to gender across a range of system operating points.
Perceptual-based deep-learning denoiser as a defense against adversarial attacks on ASR systems
In this paper we investigate speech denoising as a defense against adversarial attacks on automatic speech recognition (ASR) systems.
Automated Evaluation Of Psychotherapy Skills Using Speech And Language Technologies
With the growing prevalence of psychological interventions, it is vital to have measures which rate the effectiveness of psychological care to assist in training, supervision, and quality assurance of services.
Adversarial Attack and Defense Strategies for Deep Speaker Recognition Systems
Robust speaker recognition, including in the presence of malicious attacks, is becoming increasingly important and essential, especially due to the proliferation of several smart speakers and personal agents that interact with an individual's voice commands to perform diverse, and even sensitive tasks.
Robust speaker recognition using unsupervised adversarial invariance
In this paper, we address the problem of speaker recognition in challenging acoustic conditions using a novel method to extract robust speaker-discriminative speech representations.
