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.

本文提出了一种基于 MLPMixer 的纯 MLP 神经网络模型，用于识别开放词汇下的关键字，该模型在 Hey-Snips 数据集和内部数据集中都取得了比传统基于循环神经网络和卷积神经网络模型更好的性能，并拥有更少的参数和 MACs。