Model Shrinking for Embedded Keyword Spotting

@article{Sun2015ModelSF,
  title={Model Shrinking for Embedded Keyword Spotting},
  author={Ming Sun and Varun K. Nagaraja and Bj{\"o}rn Hoffmeister and Shiv Vitaladevuni},
  journal={2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)},
  year={2015},
  pages={369-374}
}
In this paper we present two approaches to improve computational efficiency of a keyword spotting system running on a resource constrained device. This embedded keyword spotting system detects a pre-specified keyword in real time at low cost of CPU and memory. Our system is a two stage cascade. The first stage extracts keyword hypotheses from input audio streams. After the first stage is triggered, hand-crafted features are extracted from the keyword hypothesis and fed to a support vector… CONTINUE READING

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An application of recurrent neural networks to discriminative keyword spotting

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