Audiomer: A Convolutional Transformer for Keyword Spotting

• URL: http://arxiv.org/abs/2109.10252v1
• Date: Tue, 21 Sep 2021 15:28:41 GMT
• Title: Audiomer: A Convolutional Transformer for Keyword Spotting
• Authors: Surya Kant Sahu, Sai Mitheran, Juhi Kamdar, Meet Gandhi
• Abstract summary: We introduce Audiomer, where we combine 1D Residual Networks with Performer Attention to achieve state-of-the-art performance in Keyword Spotting. Audiomer allows for deployment in compute-constrained devices and training on smaller datasets.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Transformers have seen an unprecedented rise in Natural Language Processing and Computer Vision tasks. However, in audio tasks, they are either infeasible to train due to extremely large sequence length of audio waveforms or reach competitive performance after feature extraction through Fourier-based methods, incurring a loss-floor. In this work, we introduce an architecture, Audiomer, where we combine 1D Residual Networks with Performer Attention to achieve state-of-the-art performance in Keyword Spotting with raw audio waveforms, out-performing all previous methods while also being computationally cheaper, much more parameter and data-efficient. Audiomer allows for deployment in compute-constrained devices and training on smaller datasets.

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