Convolutional Recurrent Neural Networks for Small-Footprint Keyword Spotting

@inproceedings{Arik2017ConvolutionalRN,
  title={Convolutional Recurrent Neural Networks for Small-Footprint Keyword Spotting},
  author={Sercan {\"O}mer Arik and Markus Kliegl and Rewon Child and Joel Hestness and Andrew Gibiansky and Christopher Fougner and Ryan Prenger and Adam Coates},
  booktitle={INTERSPEECH},
  year={2017}
}
Keyword spotting (KWS) constitutes a major component of human-technology interfaces. Maximizing the detection accuracy at a low false alarm (FA) rate, while minimizing the footprint size, latency and complexity are the goals for KWS. Towards achieving them, we study Convolutional Recurrent Neural Networks (CRNNs). Inspired by large-scale state-ofthe-art speech recognition systems, we combine the strengths of convolutional layers and recurrent layers to exploit local structure and long-range… CONTINUE READING
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An Experimental Analysis of the Power Consumption of Convolutional Neural Networks for Keyword Spotting

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) • 2018
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Deep Residual Learning for Small-Footprint Keyword Spotting

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) • 2018
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