activity
20192022
most citedCMKD: CNN/Transformer-Based Cross-Model Knowledge Distillation for Audio Classification

17 citations · 43 across the 6 of their papers we have counts for

collaborators

7 papers

eess.AS2022

On Unsupervised Uncertainty-Driven Speech Pseudo-Label Filtering and Model Calibration

Nauman Dawalatabad, Sameer Khurana, Antoine Laurent +1

Pseudo-label (PL) filtering forms a crucial part of Self-Training (ST) methods for unsupervised domain adaptation. Dropout-based Uncertainty-driven Self-Training (DUST) proceeds by…

cs.SD202217 cited

CMKD: CNN/Transformer-Based Cross-Model Knowledge Distillation for Audio Classification

Yuan Gong, Sameer Khurana, Andrew Rouditchenko +1

Audio classification is an active research area with a wide range of applications. Over the past decade, convolutional neural networks (CNNs) have been the de-facto standard buildi…

cs.CL202112 cited

PARP: Prune, Adjust and Re-Prune for Self-Supervised Speech Recognition

Cheng-I Jeff Lai, Yang Zhang, Alexander H. Liu +7

Self-supervised speech representation learning (speech SSL) has demonstrated the benefit of scale in learning rich representations for Automatic Speech Recognition (ASR) with limit…

cs.CL2020

Unsupervised Domain Adaptation for Speech Recognition via Uncertainty Driven Self-Training

Sameer Khurana, Niko Moritz, Takaaki Hori +1

The performance of automatic speech recognition (ASR) systems typically degrades significantly when the training and test data domains are mismatched. In this paper, we show that s…

eess.AS20209 cited

CSTNet: Contrastive Speech Translation Network for Self-Supervised Speech Representation Learning

Sameer Khurana, Antoine Laurent, James Glass

More than half of the 7,000 languages in the world are in imminent danger of going extinct. Traditional methods of documenting language proceed by collecting audio data followed by…

eess.AS2020

A Convolutional Deep Markov Model for Unsupervised Speech Representation Learning

Sameer Khurana, Antoine Laurent, Wei-Ning Hsu +4

Probabilistic Latent Variable Models (LVMs) provide an alternative to self-supervised learning approaches for linguistic representation learning from speech. LVMs admit an intuitiv…