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Far-field speech recognition is a challenging task that conventionally usessignal processing beamforming to attack noise and interference problem. But theperformance has been found usually limited due to heavy reliance onenvironmental assumption. In this paper, we propose a unified multichannelfar-field speech recognition system that combines the neural beamforming andtransformer-based Listen, Spell, Attend (LAS) speech recognition system, whichextends the end-to-end speech recognition system further to include speechenhancement. Such framework is then jointly trained to optimize the finalobjective of interest. Specifically, factored complex linear projection (fCLP)has been adopted to form the neural beamforming. Several pooling strategies tocombine look directions are then compared in order to find the optimalapproach. Moreover, information of the source direction is also integrated inthe beamforming to explore the usefulness of source direction as a prior, whichis usually available especially in multi-modality scenario. Experiments ondifferent microphone array geometry are conducted to evaluate the robustnessagainst spacing variance of microphone array. Large in-house databases are usedto evaluate the effectiveness of the proposed framework and the proposed methodachieve 19.26\% improvement when compared with a strong baseline.

A unified multichannel far-field speech recognition system: combining neural beamforming with attention based end-to-end model