Transformer-based End-to-End Speech Recognition with Local Dense Synthesizer Attention

23 Oct 2020 Menglong Xu Shengqiang Li Xiao-Lei Zhang

Recently, several studies reported that dot-product selfattention (SA) may not be indispensable to the state-of-theart Transformer models. Motivated by the fact that dense synthesizer attention (DSA), which dispenses with dot products and pairwise interactions, achieved competitive results in many language processing tasks, in this paper, we first propose a DSA-based speech recognition, as an alternative to SA... (read more)

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