  Current keyword spotting systems are typically trained with a large amount of
pre-defined keywords. Recognizing keywords in an open-vocabulary setting is
essential for personalizing smart device interaction. Towards this goal, we
propose a pure MLP-based neural network that is based on MLPMixer - an MLP
model architecture that effectively replaces the attention mechanism in Vision
Transformers. We investigate different ways of adapting the MLPMixer
architecture to the QbyE open-vocabulary keyword spotting task. Comparisons
with the state-of-the-art RNN and CNN models show that our method achieves
better performance in challenging situations (10dB and 6dB environments) on
both the publicly available Hey-Snips dataset and a larger scale internal
dataset with 400 speakers. Our proposed model also has a smaller number of
parameters and MACs compared to the baseline models.
