Metric Learning using a Siamese Deep Neural Network



This work uses a siamese architecture to learn a similarity measure. We apply two different samples over two identical sub-networks with the same set of weights. The input of each network is based on statistical information of speech data. We can then compute the distance between both informations. The DNN is able to reduce the dimensionality of the input because it learns an invariant mapping. We present the results of the learned similarity metric using different kind of informations and compare them to classic metrics based on PLDA or cosine similarity applied to i-vectors.