Attention-based Neural Beamforming Layers for Multi-channel Speech Recognition

• URL: http://arxiv.org/abs/2105.05920v1
• Date: Wed, 12 May 2021 19:32:24 GMT
• Title: Attention-based Neural Beamforming Layers for Multi-channel Speech Recognition
• Authors: Bhargav Pulugundla, Yang Gao, Brian King, Gokce Keskin, Harish Mallidi, Minhua Wu, Jasha Droppo, Roland Maas
• Abstract summary: We propose a 2D Conv-Attention module which combines convolution neural networks with attention for beamforming. We apply self- and cross-attention to explicitly model the correlations within and between the input channels. The results show a relative improvement of 3.8% in WER by the proposed model over the baseline neural beamformer.
• Score: 17.009051842682677
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Attention-based beamformers have recently been shown to be effective for multi-channel speech recognition. However, they are less capable at capturing local information. In this work, we propose a 2D Conv-Attention module which combines convolution neural networks with attention for beamforming. We apply self- and cross-attention to explicitly model the correlations within and between the input channels. The end-to-end 2D Conv-Attention model is compared with a multi-head self-attention and superdirective-based neural beamformers. We train and evaluate on an in-house multi-channel dataset. The results show a relative improvement of 3.8% in WER by the proposed model over the baseline neural beamformer.

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