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arXiv · 1711.08058

Multiple-Instance, Cascaded Classification for Keyword Spotting in Narrow-Band Audio

Ahmad AbdulKader·Kareem Nassar·Mohamed El-Geish·Daniel Galvez·Chetan Patil
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Abstract

We propose using cascaded classifiers for a keyword spotting (KWS) task on narrow-band (NB), 8kHz audio acquired in non-IID environments -- a more challenging task than most state-of-the-art KWS systems face. We present a model that incorporates Deep Neural Networks (DNNs), cascading, multiple-feature representations, and multiple-instance learning. The cascaded classifiers handle the task's class imbalance and reduce power consumption on computationally-constrained devices via early termination. The KWS system achieves a false negative rate of 6% at an hourly false positive rate of 0.75

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Ahmad AbdulKader, Kareem Nassar, Mohamed El-Geish, Daniel Galvez, Chetan Patil. 2017-11-21. Multiple-Instance, Cascaded Classification for Keyword Spotting in Narrow-Band Audio. https://arxiv.org/abs/1711.08058

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