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20182024
most citedCharacterizing dynamically varying acoustic scenes from egocentric audio recordings in workplace setting

2 citations · 2 across the 3 of their papers we have counts for

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5 papers · 1 filter

eess.AS2020

Adversarial defense for deep speaker recognition using hybrid adversarial training

Monisankha Pal, Arindam Jati, Raghuveer Peri +3

Deep neural network based speaker recognition systems can easily be deceived by an adversary using minuscule imperceptible perturbations to the input speech samples. These adversar…

eess.AS2020

Adversarial Attack and Defense Strategies for Deep Speaker Recognition Systems

Arindam Jati, Chin-Cheng Hsu, Monisankha Pal +3

Robust speaker recognition, including in the presence of malicious attacks, is becoming increasingly important and essential, especially due to the proliferation of several smart s…

eess.AS2020

An empirical analysis of information encoded in disentangled neural speaker representations

Raghuveer Peri, Haoqi Li, Krishna Somandepalli +2

The primary characteristic of robust speaker representations is that they are invariant to factors of variability not related to speaker identity. Disentanglement of speaker repres…

eess.AS20192 cited

Characterizing dynamically varying acoustic scenes from egocentric audio recordings in workplace setting

Arindam Jati, Amrutha Nadarajan, Karel Mundnich +1

Devices capable of detecting and categorizing acoustic scenes have numerous applications such as providing context-aware user experiences. In this paper, we address the task of cha…

eess.AS2019

Robust speaker recognition using unsupervised adversarial invariance

Raghuveer Peri, Monisankha Pal, Arindam Jati +2

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…