  In recent years, neural network-based Wake Word Spotting achieves good
performance on clean audio samples but struggles in noisy environments.
Audio-Visual Wake Word Spotting (AVWWS) receives lots of attention because
visual lip movement information is not affected by complex acoustic scenes.
Previous works usually use simple addition or concatenation for multi-modal
fusion. The inter-modal correlation remains relatively under-explored. In this
paper, we propose a novel module called Frame-Level Cross-Modal Attention
(FLCMA) to improve the performance of AVWWS systems. This module can help model
multi-modal information at the frame-level through synchronous lip movements
and speech signals. We train the end-to-end FLCMA based Audio-Visual Conformer
and further improve the performance by fine-tuning pre-trained uni-modal models
for the AVWWS task. The proposed system achieves a new state-of-the-art result
(4.57% WWS score) on the far-field MISP dataset.
