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This paper further explores our previous wake word spotting system ranked2-nd in Track 1 of the MISP Challenge 2021. First, we investigate a robustunimodal approach based on 3D and 2D convolution and adopt the simple attentionmodule (SimAM) for our system to improve performance. Second, we exploredifferent combinations of data augmentation methods for better performance.Finally, we study the fusion strategies, including score-level, cascaded andneural fusion. Our proposed multimodal system leverages multimodal features anduses the complementary visual information to mitigate the performancedegradation of audio-only systems in complex acoustic scenarios. Our systemobtains a false reject rate of 2.15% and a false alarm rate of 3.44% in theevaluation set of the competition database, which achieves the newstate-of-the-art performance by 21% relative improvement compared to previoussystems.

The DKU Post-Challenge Audio-Visual Wake Word Spotting System for the 2021 MISP Challenge: Deep Analysis