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

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