Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Discriminative features using Center Loss and Reconstruction as
  Regularizer for Speech Emotion Recognition   This paper proposes a Convolutional Neural Network (CNN) inspired by
Multitask Learning (MTL) and based on speech features trained under the joint
supervision of softmax loss and center loss, a powerful metric learning
strategy, for the recognition of emotion in speech. Speech features such as
Spectrograms and Mel-frequency Cepstral Coefficient s (MFCCs) help retain
emotion-related low-level characteristics in speech. We experimented with
several Deep Neural Network (DNN) architectures that take in speech features as
input and trained them under both softmax and center loss, which resulted in
highly discriminative features ideal for Speech Emotion Recognition (SER). Our
networks also employ a regularizing effect by simultaneously performing the
auxiliary task of reconstructing the input speech features. This sharing of
representations among related tasks enables our network to better generalize
the original task of SER. Some of our proposed networks contain far fewer
parameters when compared to state-of-the-art architectures.

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The categories of this paper are: dg-ga
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The categories of this paper are: comp-gas
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The categories of this paper are: hep-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: hep-lat
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The categories of this paper are: funct-an
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The categories of this paper are: atom-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: q-bio
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The categories of this paper are: nucl-ex
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The categories of this paper are: nucl-th
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The categories of this paper are: gr-qc
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The categories of this paper are: chem-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: solv-int