Transferable Deep Features for Keyword Spotting

Proceedings

Retsinas, George¹ 

关键词: deep features;    keyword spotting;    manifold learning;    transferable features;   

DOI  :  10.3390/proceedings2020089

学科分类：社会科学、人文和艺术（综合）

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY