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Libri-Adapt: a New Speech Dataset for Unsupervised Domain Adaptation

Akhil Mathur, Fahim Kawsar, Nadia Berthouze, Nicholas D. Lane
<span title="">2020</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/rc5jnc4ldvhs3dswicq5wk3vsq" style="color: black;">ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)</a> </i> &nbsp;
This paper introduces a new dataset, Libri-Adapt, to support unsupervised domain adaptation research on speech recognition models.  ...  Built on top of the LibriSpeech corpus, Libri-Adapt contains English speech recorded on mobile and embedded-scale microphones, and spans 72 different domains that are representative of the challenging  ...  In this work, we present a new dataset named Libri-Adapt, primarily developed to study unsupervised domain adaptation, which contains domain shifts caused by a) variability in recording microphones, b)  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/icassp40776.2020.9053074">doi:10.1109/icassp40776.2020.9053074</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/icassp/MathurKBL20.html">dblp:conf/icassp/MathurKBL20</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qmmg4jdl7zdyncn3ye7tbv2gli">fatcat:qmmg4jdl7zdyncn3ye7tbv2gli</a> </span>
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