no code implementations • 1 Jan 2021 • Akhil Mathur, Shaoduo Gan, Anton Isopoussu, Fahim Kawsar, Nadia Berthouze, Nicholas Donald Lane
Breakthroughs in unsupervised domain adaptation (uDA) have opened up the possibility of adapting models from a label-rich source domain to unlabeled target domains.
1 code implementation • 6 Sep 2020 • Akhil Mathur, Fahim Kawsar, Nadia Berthouze, Nicholas D. Lane
This paper introduces a new dataset, Libri-Adapt, to support unsupervised domain adaptation research on speech recognition models.
no code implementations • 27 Mar 2020 • Akhil Mathur, Anton Isopoussu, Fahim Kawsar, Nadia Berthouze, Nicholas D. Lane
A major challenge in building systems that combine audio models with commodity microphones is to guarantee their accuracy and robustness in the real-world.
no code implementations • 25 Sep 2019 • Akhil Mathur, Shaoduo Gan, Anton Isopoussu, Fahim Kawsar, Nadia Berthouze, Nicholas D. Lane
Despite the recent breakthroughs in unsupervised domain adaptation (uDA), no prior work has studied the challenges of applying these methods in practical machine learning scenarios.