Corpus ID: 195316945

Learning Discriminative features using Center Loss and Reconstruction as Regularizer for Speech Emotion Recognition

@article{Tripathi2019LearningDF,
  title={Learning Discriminative features using Center Loss and Reconstruction as Regularizer for Speech Emotion Recognition},
  author={Suraj Tripathi and Abhiram Ramesh and Abhay Kumar and Chirag Singh and Promod Yenigalla},
  journal={ArXiv},
  year={2019},
  volume={abs/1906.08873}
}
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… Expand
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