QbyE-MLPMixer: Query-by-Example Open-Vocabulary Keyword Spotting using MLPMixer

• URL: http://arxiv.org/abs/2206.13231v1
• Date: Thu, 23 Jun 2022 18:18:44 GMT
• Title: QbyE-MLPMixer: Query-by-Example Open-Vocabulary Keyword Spotting using MLPMixer
• Authors: Jinmiao Huang, Waseem Gharbieh, Qianhui Wan, Han Suk Shim, Chul Lee
• Abstract summary: Current keyword spotting systems are typically trained with a large amount of pre-defined keywords. We propose a pure-vocabulary neural network that is based on theMixer model architecture. Our proposed model has a smaller number of parameters and MACs compared to the baseline models.
• Score: 10.503972720941693
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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