Analysis of emotion recognition from cross-lingual speech: Arabic, English, and Urdu

Moomal Farhad, Heba Ismail, Saad Harous, Mohammad Mehedy Masud, Azam Beg

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In a system which involves interaction be- tween machines and humans, the recognition of emotion from audio has always been a focus of research. Emotion recognition can play an essential role in many fields, such as medicine, law, psychology, and customer services. In this paper, we present an empirical comparative analysis of several machine learning classifiers for emotion recognition in audio data. Evaluations are performed for a set of predefined emotions such as happy, sad, and angry from Arabic, English, and Urdu languages. Pitch and cepstral features are extracted from audio files and principal component analysis is applied for dimensionality reduction. Experiments show that random forest outperformed other classifiers on Urdu dataset with an accuracy of 78.75%. However, the performance of Meta iterative classifier on Arabic dataset was better than random forest and neural network with the accuracy of 70%. Classification of emotions on the English dataset, which do not differ much in terms of pitch and MFCC features, generated the lowest accuracies at or below 31%.

Original languageEnglish
Title of host publicationProceedings of 2nd International Conference on Computation, Automation and Knowledge Management, ICCAKM 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages42-47
Number of pages6
ISBN (Electronic)9781728194912
DOIs
Publication statusPublished - Jan 19 2021
Event2nd International Conference on Computation, Automation and Knowledge Management, ICCAKM 2021 - Dubai, United Arab Emirates
Duration: Jan 19 2021Jan 21 2021

Publication series

NameProceedings of 2nd International Conference on Computation, Automation and Knowledge Management, ICCAKM 2021

Conference

Conference2nd International Conference on Computation, Automation and Knowledge Management, ICCAKM 2021
Country/TerritoryUnited Arab Emirates
CityDubai
Period1/19/211/21/21

Keywords

  • Arabic
  • Emotion recognition
  • English
  • Machine learning
  • Urdu

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems and Management
  • Control and Optimization

Fingerprint

Dive into the research topics of 'Analysis of emotion recognition from cross-lingual speech: Arabic, English, and Urdu'. Together they form a unique fingerprint.

Cite this