Small-Footprint Keyword Spotting
7 papers with code • 0 benchmarks • 0 datasets
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Latest papers with no code
Small-footprint Keyword Spotting Using Deep Neural Network and Connectionist Temporal Classifier
Mainly for the sake of solving the lack of keyword-specific data, we propose one Keyword Spotting (KWS) system using Deep Neural Network (DNN) and Connectionist Temporal Classifier (CTC) on power-constrained small-footprint mobile devices, taking full advantage of general corpus from continuous speech recognition which is of great amount.
Max-Pooling Loss Training of Long Short-Term Memory Networks for Small-Footprint Keyword Spotting
Finally, the max-pooling loss trained LSTM initialized with a cross-entropy pre-trained network shows the best performance, which yields $67. 6\%$ relative reduction compared to baseline feed-forward DNN in Area Under the Curve (AUC) measure.
Convolutional Recurrent Neural Networks for Small-Footprint Keyword Spotting
Keyword spotting (KWS) constitutes a major component of human-technology interfaces.
