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Depthwise-Separable Residual Capsule for Robust Keyword Spotting

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MultiMedia Modeling (MMM 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13142))

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Abstract

Keyword spotting is widely used in device wake-up and user interaction of smart devices. However, the resources in smart devices are limited. In order to ensure that the keyword spotting system can always run efficiently and accurately in smart devices, the size of the model must be compressed to realize a small and compact model. In addition, in actual application scenarios, noise, speech rate, and overlapped speech pose great challenges to the robustness of the model. In order to solve the above problems, we propose a depthwise-separable residual capsule neural network, which uses depthwise-separable convolution to achieve a more compact design, and also uses a multi-scale capsule classifier to improve the model’s robustness in the above complex scenarios. We have achieved the best accuracy on the Google Command dataset, and have fewer calculations and fewer parameters than the previous methods.

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Correspondence to Xianghong Huang .

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Huang, X., Yang, Q., Liu, S. (2022). Depthwise-Separable Residual Capsule for Robust Keyword Spotting. In: Þór Jónsson, B., et al. MultiMedia Modeling. MMM 2022. Lecture Notes in Computer Science, vol 13142. Springer, Cham. https://doi.org/10.1007/978-3-030-98355-0_17

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  • DOI: https://doi.org/10.1007/978-3-030-98355-0_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-98354-3

  • Online ISBN: 978-3-030-98355-0

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