Speech emotion recognition with light gradient boosting decision trees machine

Kah Liang Ong, Chin Poo Lee, Heng Siong Lim, Kian Ming Lim

Research output: Journal PublicationArticlepeer-review

6 Citations (Scopus)


Speech emotion recognition aims to identify the emotion expressed in the speech by analyzing the audio signals. In this work, data augmentation is first performed on the audio samples to increase the number of samples for better model learning. The audio samples are comprehensively encoded as the frequency and temporal domain features. In the classification, a light gradient boosting machine is leveraged. The hyperparameter tuning of the light gradient boosting machine is performed to determine the optimal hyperparameter settings. As the speech emotion recognition datasets are imbalanced, the class weights are regulated to be inversely proportional to the sample distribution where minority classes are assigned higher class weights. The experimental results demonstrate that the proposed method outshines the state-of-the-art methods with 84.91% accuracy on the Berlin database of emotional speech (emo-DB) dataset, 67.72% on the Ryerson audio-visual database of emotional speech and song (RAVDESS) dataset, and 62.94% on the interactive emotional dyadic motion capture (IEMOCAP) dataset.

Original languageEnglish
Pages (from-to)4020-4028
Number of pages9
JournalInternational Journal of Electrical and Computer Engineering
Issue number4
Publication statusPublished - Aug 2023
Externally publishedYes


  • Light gradient boosting machine
  • Machine learning
  • Speech
  • Speech emotion
  • Speech emotion recognition

ASJC Scopus subject areas

  • General Computer Science
  • Electrical and Electronic Engineering


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