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Opening the Black Box of wav2vec Feature Encoder. (arXiv:2210.15386v1 [cs.SD])
Oct. 28, 2022, 1:11 a.m. | Kwanghee Choi, Eun Jung Yeo
cs.LG updates on arXiv.org arxiv.org
Self-supervised models, namely, wav2vec and its variants, have shown
promising results in various downstream tasks in the speech domain. However,
their inner workings are poorly understood, calling for in-depth analyses on
what the model learns. In this paper, we concentrate on the convolutional
feature encoder where its latent space is often speculated to represent
discrete acoustic units. To analyze the embedding space in a reductive manner,
we feed the synthesized audio signals, which is the summation of simple sine
waves. …
More from arxiv.org / cs.LG updates on arXiv.org
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