  This letter proposes a new wake word detection system based on Res2Net. As a
variant of ResNet, Res2Net was first applied to objection detection. Res2Net
realizes multiple feature scales by increasing possible receptive fields. This
multiple scaling mechanism significantly improves the detection ability of wake
words with different durations. Compared with the ResNet-based model, Res2Net
also significantly reduces the model size and is more suitable for detecting
wake words. The proposed system can determine the positions of wake words from
the audio stream without any additional assistance. The proposed method is
verified on the Mobvoi dataset containing two wake words. At a false alarm rate
of 0.5 per hour, the system reduced the false rejection of the two wake words
by more than 12% over prior works.
