  This paper presents the details of our system designed for the Task 1 of
Multimodal Information Based Speech Processing (MISP) Challenge 2021. The
purpose of Task 1 is to leverage both audio and video information to improve
the environmental robustness of far-field wake word spotting. In the proposed
system, firstly, we take advantage of speech enhancement algorithms such as
beamforming and weighted prediction error (WPE) to address the multi-microphone
conversational audio. Secondly, several data augmentation techniques are
applied to simulate a more realistic far-field scenario. For the video
information, the provided region of interest (ROI) is used to obtain visual
representation. Then the multi-layer CNN is proposed to learn audio and visual
representations, and these representations are fed into our two-branch
attention-based network which can be employed for fusion, such as transformer
and conformed. The focal loss is used to fine-tune the model and improve the
performance significantly. Finally, multiple trained models are integrated by
casting vote to achieve our final 0.091 score.
