Till now, attention-based models have been used with great success in the keyword spotting problem domain. However, in light of recent advances in deep learning, the question arises whether self-attention is truly irreplaceable for recognizing speech keywords. We thus explore the usage of gated MLPs --previously shown to be alternatives to transformers in vision tasks-- for the keyword spotting task. We provide a family of highly efficient MLP-based models for keyword spotting, with less than 0.5 million parameters. We show that our approach achieves competitive performance on Google Speech Commands V2-12 and V2-35 benchmarks with much fewer parameters than self-attention-based methods.
@article{arxiv.2110.07749,
title = {Attention-Free Keyword Spotting},
author = {Mashrur M. Morshed and Ahmad Omar Ahsan},
journal= {arXiv preprint arXiv:2110.07749},
year = {2022}
}