mlsanityData to 7 Oct 2026

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

arXiv 2206.13231 ↗First posted 23 Jun 2022eess.AS cs.CL cs.LGcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 20 of its 37 references that are in the map. First posted on arXiv 23 Jun 2022.

Builds on

20 of 37 references.

References · 20

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8 more
  1. 2016Discriminative Acoustic Word Embeddings: Recurrent Neural Network-Based Approachescited by 25
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 1; 2025: 0; 2026: 1.

4 papers.