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BYOL-S: Learning Self-supervised Speech Representations by Bootstrapping. (arXiv:2206.12038v1 [cs.SD])
June 27, 2022, 1:10 a.m. | Gasser Elbanna, Neil Scheidwasser-Clow, Mikolaj Kegler, Pierre Beckmann, Karl El Hajal, Milos Cernak
cs.LG updates on arXiv.org arxiv.org
Methods for extracting audio and speech features have been studied since
pioneering work on spectrum analysis decades ago. Recent efforts are guided by
the ambition to develop general-purpose audio representations. For example,
deep neural networks can extract optimal embeddings if they are trained on
large audio datasets. This work extends existing methods based on
self-supervised learning by bootstrapping, proposes various encoder
architectures, and explores the effects of using different pre-training
datasets. Lastly, we present a novel training framework to come …
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