15 citations · 24 across the 25 of their papers we have counts for
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eess.AS2023
FusDom: Combining In-Domain and Out-of-Domain Knowledge for Continuous Self-Supervised Learning
Ashish Seth, Sreyan Ghosh, S. Umesh +1
Continued pre-training (CP) offers multiple advantages, like target domain adaptation and the potential to exploit the continuous stream of unlabeled data available online. However…
eess.AS2023
Stable Distillation: Regularizing Continued Pre-training for Low-Resource Automatic Speech Recognition
Ashish Seth, Sreyan Ghosh, S. Umesh +1
Continued self-supervised (SSL) pre-training for adapting existing SSL models to the target domain has shown to be extremely effective for low-resource Automatic Speech Recognition…
eess.AS2023
UNFUSED: UNsupervised Finetuning Using SElf supervised Distillation
Ashish Seth, Sreyan Ghosh, S. Umesh +1
In this paper, we introduce UnFuSeD, a novel approach to leverage self-supervised learning and reduce the need for large amounts of labeled data for audio classification. Unlike pr…