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Open AccessProceedings

Transferable Deep Features for Keyword Spotting

1
CIL/IIT/NCSR “Demokritos”, GR-15310 Agia Paraskevi, Greece
2
School of Electrical and Computer Engineering, National Technical University of Athens, GR-15773 Athens, Greece
*
Author to whom correspondence should be addressed.
Presented at the International Workshop on Computational Intelligence for Multimedia Understanding (IWCIM), Kos Island, Greece, 2 September 2017.
Proceedings 2018, 2(2), 89; https://doi.org/10.3390/proceedings2020089
Published: 9 January 2018
Deep features, defined as the activations of hidden layers of a neural network, have given promising results applied to various vision tasks. In this paper, we explore the usefulness and transferability of deep features, applied in the context of the problem of keyword spotting (KWS). We use a state-of-the-art deep convolutional network to extract deep features. The optimal parameters concerning their application are subsequently studied: the impact of the choice of hidden layer, the impact of applying dimensionality reduction with a manifold learning technique, as well as the choice of dissimilarity measure used to retrieve relevant word images. Extensive numerical results show that deep features lead to state-of-the-art KWS performance, even when the test and training set come from different document collections.
Keywords: deep features; keyword spotting; manifold learning; transferable features deep features; keyword spotting; manifold learning; transferable features
MDPI and ACS Style

Retsinas, G.; Sfikas, G.; Gatos, B. Transferable Deep Features for Keyword Spotting. Proceedings 2018, 2, 89.

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