arXiv:2008.00209
공식 코드 · Papers with Code
Neural ODE with Temporal Convolution and Time Delay Neural Networks for Small-Footprint Keyword Spotting
원문 초록
In this paper, we propose neural network models based on the neural ordinary differential equation (NODE) for small-footprint keyword spotting (KWS). We present techniques to apply NODE to KWS that make it possible to adopt Batch Normalization to NODE-based network and to reduce the number of computations during inference. Finally, we show that the number of model parameters of the proposed model is smaller by 68% than that of the conventional KWS model.
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Advances in Small-Footprint Keyword Spotting: A Comprehensive Review of Efficient Models and Algorithms
Small-Footprint Keyword Spotting with Multi-Scale Temporal Convolution
AnalyticKWS: Towards Exemplar-Free Analytic Class Incremental Learning for Small-footprint Keyword Spotting
Disentangled Training with Adversarial Examples For Robust Small-footprint Keyword Spotting
DCCRN-KWS: an audio bias based model for noise robust small-footprint keyword spotting
Small-footprint slimmable networks for keyword spotting
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