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AutoKWS: Keyword Spotting with Differentiable Architecture Search
Smart audio devices are gated by an always-on lightweight keyword spotti...
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Performance-Oriented Neural Architecture Search
Hardware-Software Co-Design is a highly successful strategy for improvin...
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NASGEM: Neural Architecture Search via Graph Embedding Method
Neural Architecture Search (NAS) automates and prospers the design of ne...
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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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Genetic Neural Architecture Search for automatic assessment of human sperm images
Male infertility is a disease which affects approximately 7 morphology a...
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RandomNet: Towards Fully Automatic Neural Architecture Design for Multimodal Learning
Almost all neural architecture search methods are evaluated in terms of ...
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Neural Architecture Construction using EnvelopeNets
In recent years, advances in the design of convolutional neural networks...
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Neural Architecture Search For Keyword Spotting
Deep neural networks have recently become a popular solution to keyword spotting systems, which enable the control of smart devices via voice. In this paper, we apply neural architecture search to search for convolutional neural network models that can help boost the performance of keyword spotting based on features extracted from acoustic signals while maintaining an acceptable memory footprint. Specifically, we use differentiable architecture search techniques to search for operators and their connections in a predefined cell search space. The found cells are then scaled up in both depth and width to achieve competitive performance. We evaluated the proposed method on Google's Speech Commands Dataset and achieved a state-of-the-art accuracy of over 97 setting of 12-class utterance classification commonly reported in the literature.
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