  When a sufficiently large far-field training data is presented, jointly
optimizing a multichannel frontend and an end-to-end (E2E) Automatic Speech
Recognition (ASR) backend shows promising results. Recent literature has shown
traditional beamformer designs, such as MVDR (Minimum Variance Distortionless
Response) or fixed beamformers can be successfully integrated as the frontend
into an E2E ASR system with learnable parameters. In this work, we propose the
self-attention channel combinator (SACC) ASR frontend, which leverages the
self-attention mechanism to combine multichannel audio signals in the magnitude
spectral domain. Experiments conducted on a multichannel playback test data
shows that the SACC achieved a 9.3% WERR compared to a state-of-the-art fixed
beamformer-based frontend, both jointly optimized with a ContextNet-based ASR
backend. We also demonstrate the connection between the SACC and the
traditional beamformers, and analyze the intermediate outputs of the SACC.
