Understanding Self-Attention of Self-Supervised Audio Transformers

5 Jun 2020Shu-wen YangAndy T. LiuHung-yi Lee

Self-supervised Audio Transformers (SAT) enable great success in many downstream speech applications like ASR, but how they work has not been widely explored yet. In this work, we present multiple strategies for the analysis of attention mechanisms in SAT... (read more)

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