Speech Emotion Recognition Using Multi-Layer Perceptron Classifier

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1 Citation (Scopus)

Abstract

This paper proposes a speech emotion recognition approach using the Multi-Layer Perceptron Classifier (MLP Classifier). The Mel-Frequency Cepstral Coefficients feature and openSMILE feature are respectively extracted. With the extracted features, MLP Classifier is used to classify the speech emotion. The Berlin database which contains seven emotions: happiness, anger, anxiety, fear, boredom and disgust, is used to evaluate the performance of the proposed approach. Data augmentation are furtherly employed and experimental results show that the proposed approach achieves satisfied performances. Comparisons are conducted when with data augmentation and without data augmentation, and the results indicate better performance with data augmentation.

Original languageEnglish
Title of host publication2022 IEEE 10th International Conference on Information, Communication and Networks, ICICN 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages644-648
Number of pages5
ISBN (Electronic)9781665490825
DOIs
Publication statusPublished - 2022
Event10th IEEE International Conference on Information, Communication and Networks, ICICN 2022 - Zhangye, China
Duration: 23 Aug 202224 Aug 2022

Publication series

Name2022 IEEE 10th International Conference on Information, Communication and Networks, ICICN 2022

Conference

Conference10th IEEE International Conference on Information, Communication and Networks, ICICN 2022
Country/TerritoryChina
CityZhangye
Period23/08/2224/08/22

Keywords

  • mel-frequency cepstral coefficients
  • multi-layer perceptron classifier
  • openSMILE Feature
  • speech emotion recognition

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