Abstract
Conformer has achieved impressive results in Automatic Speech Recognition (ASR) by leveraging transformer's capturing of content-based global interactions and convolutional neural network's exploiting of local features. In Conformer, two macaron-like feed-forward layers with half-step residual connections sandwich the multi-head self-attention and convolution modules followed by a post layer normalization. We improve Conformer's long-sequence representation ability in two directions, \emph{sparser} and \emph{deeper}. We adapt a sparse self-attention mechanism with $\mathcal{O}(L\text{log}L)$ in time complexity and memory usage. A deep normalization strategy is utilized when performing residual connections to ensure our training of hundred-level Conformer blocks. On the Japanese CSJ-500h dataset, this deep sparse Conformer achieves respectively CERs of 5.52\%, 4.03\% and 4.50\% on the three evaluation sets and 4.16\%, 2.84\% and 3.20\% when ensembling five deep sparse Conformer variants from 12 to 16, 17, 50, and finally 100 encoder layers.
Abstract (translated)
URL
https://arxiv.org/abs/2209.00260
