Deep Multilayer Perceptrons for Dimensional Speech Emotion Recognition

Bagus Tris Atmaja, Masato Akagi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

15 Citations (Scopus)

Abstract

Modern deep learning architectures are ordinarily performed in high performance computing facilities due to the large size of their input features and complexity of their models. This paper proposes traditional multilayer perceptrons (MLP) with deep layers and small input sizes to tackle this computation requirement limitation. This study compares a proposed deep MLP method to the more modern deep learning architectures with the same number of layers, batch size, and optimizer. The result shows that our proposed deep MLP outperformed modern deep learning architectures, i.e., LSTM and CNN, on the same number of layers and value of parameters. Both proposed and benchmark methods were optimized in the same way. The deep MLP exhibited the highest performance on both speaker-dependent and speaker-independent scenarios on IEMOCAP and MSP-IMPROV datasets.

Original languageEnglish
Title of host publication2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages325-331
Number of pages7
ISBN (Electronic)9789881476883
Publication statusPublished - 7 Dec 2020
Event2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020 - Virtual, Auckland, New Zealand
Duration: 7 Dec 202010 Dec 2020

Publication series

Name2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020 - Proceedings

Conference

Conference2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020
Country/TerritoryNew Zealand
CityVirtual, Auckland
Period7/12/2010/12/20

Keywords

  • Affective computing
  • dimensional emotion
  • multilayer perceptrons
  • neural networks
  • speech emotion recognition

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