Acoustic features extraction for emotion recognition

Jia Rong, Yi Ping Phoebe Chen, Morshed Chowdhury, Gang Li

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

22 Citations (Scopus)

Abstract

In the last decade, the efforts of spoken language processing have achieved significant advances, however, the work with emotional recognition has not progressed so far, and can only achieve 50% to 60% in accuracy [1]. This is because a majority of researchers in this field have focused on the synthesis of emotional speech rather than focusing on automating human emotion recognition. Many research groups have focused on how to improve the performance of the classifier they used for emotion recognition, and few work has been done on data pre-processing, such as the extraction and selection of a set of specifying acoustic features instead of using all the possible ones they had in hand. To work with well-selected acoustic features does not mean to delay the whole job, but this will save much time and resources by removing the irrelative information and reducing the high-dimension data calculation. In this paper, we developed an automatic feature selector based on a RF2TREE algorithm and the traditional C4.5 algorithm. RF2TREE applied here helped us to solve the problems that did not have enough data examples. The ensemble learning technique was applied to enlarge the original data set by building a bagged random forest to generate many virtual examples, and then the new data set was used to train a single decision tree, which selects the most efficient features to represent the speech signals for the emotion recognition. Finally, the output of the selector was a set of specifying acoustic features, produced by RF2TREE and a single decision tree.

Original languageEnglish
Title of host publicationProceedings - 6th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2007; 1st IEEE/ACIS International Workshop on e-Activity, IWEA 2007
Pages419-424
Number of pages6
DOIs
Publication statusPublished - 1 Dec 2007
Event6th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2007; 1st IEEE/ACIS International Workshop on e-Activity, IWEA 2007 - Melbourne, VIC, Australia
Duration: 11 Jul 200713 Jul 2007

Publication series

NameProceedings - 6th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2007; 1st IEEE/ACIS International Workshop on e-Activity, IWEA 2007

Conference

Conference6th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2007; 1st IEEE/ACIS International Workshop on e-Activity, IWEA 2007
CountryAustralia
CityMelbourne, VIC
Period11/07/0713/07/07

Keywords

  • Decision tree
  • Ensemble learning
  • Feature extraction
  • Machine learning
  • Random forest
  • Twice learning

Cite this

Rong, J., Chen, Y. P. P., Chowdhury, M., & Li, G. (2007). Acoustic features extraction for emotion recognition. In Proceedings - 6th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2007; 1st IEEE/ACIS International Workshop on e-Activity, IWEA 2007 (pp. 419-424). [4276418] (Proceedings - 6th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2007; 1st IEEE/ACIS International Workshop on e-Activity, IWEA 2007). https://doi.org/10.1109/ICIS.2007.48