TY - GEN
T1 - A Comparative Study for Language Recognition using Learning-based Approaches
AU - Chew, Chee Meng
AU - Ming Lim, Kian
AU - Lee, Chin Poo
AU - Yang Chan, Xian
AU - Lew, Ching Hong
AU - Ru Song, Veron Wei
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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%.
AB - 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%.
KW - Convolutional Neural Network
KW - Deep Learning
KW - Language Recognition
KW - Machine Learning
UR - http://www.scopus.com/inward/record.url?scp=85174415567&partnerID=8YFLogxK
U2 - 10.1109/ICoICT58202.2023.10262698
DO - 10.1109/ICoICT58202.2023.10262698
M3 - Conference contribution
AN - SCOPUS:85174415567
T3 - 2023 11th International Conference on Information and Communication Technology, ICoICT 2023
SP - 528
EP - 532
BT - 2023 11th International Conference on Information and Communication Technology, ICoICT 2023
T2 - 11th International Conference on Information and Communication Technology, ICoICT 2023
Y2 - 23 August 2023 through 24 August 2023
ER -