A Comparative Study for Language Recognition using Learning-based Approaches

Chee Meng Chew*, Kian Ming Lim, Chin Poo Lee, Xian Yang Chan, Ching Hong Lew, Veron Wei Ru Song

*Corresponding author for this work

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

Abstract

Language recognition is helpful for determining the natural language in a given document or part of text. Language recognition has attracted more attention in recent times due to its wide-ranging applications, including speech translation, multilingual speech recognition and more. Indeed, language recognition should be effective to ensure practical implementation. Therefore, learning-based approaches are introduced to enhance the effectiveness of language recognition. In this paper, a total of six learning-based approaches have been implemented for solving the language recognition problem. Experiments and evaluations are conducted to study the effectiveness of these learning-based approaches on identifying 5 different languages which are English, German, Czech, French, and Swedish. The experimental results show that the 1D-CNN model achieves the highest accuracy score of 65.99%.

Original languageEnglish
Title of host publication2023 11th International Conference on Information and Communication Technology, ICoICT 2023
Pages528-532
Number of pages5
ISBN (Electronic)9798350321982
DOIs
Publication statusPublished - 2023
Externally publishedYes
Event11th International Conference on Information and Communication Technology, ICoICT 2023 - Melaka, Malaysia
Duration: 23 Aug 202324 Aug 2023

Publication series

Name2023 11th International Conference on Information and Communication Technology, ICoICT 2023
Volume2023-August

Conference

Conference11th International Conference on Information and Communication Technology, ICoICT 2023
Country/TerritoryMalaysia
CityMelaka
Period23/08/2324/08/23

Keywords

  • Convolutional Neural Network
  • Deep Learning
  • Language Recognition
  • Machine Learning

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