Web: http://arxiv.org/abs/2209.12900

Sept. 29, 2022, 1:15 a.m. | Tung-Yu Wu, Chen-An Li, Tzu-Han Lin, Tsu-Yuan Hsu, Hung-Yi Lee

cs.CL updates on arXiv.org arxiv.org

Self-supervised learning (SSL) speech models have achieved unprecedented
success in speech representation learning, but some questions regarding their
representation ability remain unanswered. This paper addresses two of them: (1)
Can SSL speech models deal with non-speech audio?; (2) Would different SSL
speech models have insights into diverse aspects of audio features? To answer
the two questions, we conduct extensive experiments on abundant speech and
non-speech audio datasets to evaluate the representation ability of currently
state-of-the-art SSL speech models, which are …

arxiv audio speech

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