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Neural Architecture Search For Keyword Spotting
Deep neural networks have recently become a popular solution to keyword ...
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Small-Footprint Keyword Spotting on Raw Audio Data with Sinc-Convolutions
Keyword Spotting (KWS) enables speech-based user interaction on smart de...
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Stochastic Adaptive Neural Architecture Search for Keyword Spotting
The problem of keyword spotting i.e. identifying keywords in a real-time...
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Performance-Oriented Neural Architecture Search
Hardware-Software Co-Design is a highly successful strategy for improvin...
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Latency-Aware Differentiable Neural Architecture Search
Differentiable neural architecture search methods became popular in auto...
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QuickNet: Maximizing Efficiency and Efficacy in Deep Architectures
We present QuickNet, a fast and accurate network architecture that is bo...
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Multiple-Instance, Cascaded Classification for Keyword Spotting in Narrow-Band Audio
We propose using cascaded classifiers for a keyword spotting (KWS) task ...
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AutoKWS: Keyword Spotting with Differentiable Architecture Search
Smart audio devices are gated by an always-on lightweight keyword spotting program to reduce power consumption. It is however challenging to design models that have both high accuracy and low latency for accurate and fast responsiveness. Many efforts have been made to develop end-to-end neural networks, in which depthwise separable convolutions, temporal convolutions, and LSTMs are adopted as building units. Nonetheless, these networks designed with human expertise may not achieve an optimal trade-off in an expansive search space. In this paper, we propose to leverage recent advances in differentiable neural architecture search to discover more efficient networks. Our found model attains 97.2
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