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

• URL: http://arxiv.org/abs/1711.08058v2
• Date: Fri, 25 Apr 2025 04:47:20 GMT
• Title: Multiple-Instance, Cascaded Classification for Keyword Spotting in Narrow-Band Audio
• Authors: Ahmad AbdulKader, Kareem Nassar, Mohamed El-Geish, Daniel Galvez, Chetan Patil,
• Abstract summary: We propose using cascaded classifiers for a keyword spotting (KWS) task on narrow-band (NB), 8kHz audio acquired in non-IID environments.<n>We present a model that incorporates Deep Neural Networks (DNNs), cascading, multiple-feature representations, and multiple-instance learning.<n>The KWS system achieves a false negative rate of 6% at an hourly false positive rate of 0.75.
• Score: 0.4793481003355277
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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