Paralinguistic model for emotions recognition with deep neural networks

In this paper the computer paralinguistic model for emotions recognition based on deep neural networks is proposed. The main stages of its construction were studied and relevant models of the deep convolutional networks and recurrent networks with LSTM memory cell were used. Intensive computer experiments on the emotions recognition from human speech with proposed model were conducted. As the data for research and testing of our model RAVDESS dataset of audio recordings was selected. The results showed a high efficiency of the explored model, and the accuracy estimates for some classes of emotions were reached 90%.

Authors
Shchetinin E.Y.1 , Sevastianov L.A. 2, 3 , Kulyabov D.S. 2 , Ayrjan E.A. 3, 4 , Demidova A.V. 2
Publisher
Институт проблем управления им. В.А. Трапезникова РАН
Language
English
Pages
42-50
Status
Published
Year
2020
Organizations
  • 1 Financial University, Government of the Russian Federation
  • 2 Peoples' Friendship University of Russia (RUDN University)
  • 3 Joint Institute for Nuclear Research
  • 4 Dubna State University
Keywords
emotions recognition; paralinguistic model; convolutional network; ResNet18; recurrent network; BLSTM model; RAVDESS
Date of creation
06.07.2022
Date of change
06.07.2022
Short link
https://repository.rudn.ru/en/records/article/record/90280/
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Shchetinin E.Y., Sevastianov L.A., Ayrjan E.A., Demidova A.V.
Distributed computer and communication networks: control, computation, communications (DCCN-2020). Институт проблем управления им. В.А. Трапезникова РАН. 2020. P. 33-41