@inproceedings{42f693548f4345c2bd704f03b2c5311d,
title = "A Comparative Study for Language Recognition using Learning-based Approaches",
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%.",
keywords = "Convolutional Neural Network, Deep Learning, Language Recognition, Machine Learning",
author = "Chew, {Chee Meng} and {Ming Lim}, Kian and Lee, {Chin Poo} and {Yang Chan}, Xian and Lew, {Ching Hong} and {Ru Song}, {Veron Wei}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 11th International Conference on Information and Communication Technology, ICoICT 2023 ; Conference date: 23-08-2023 Through 24-08-2023",
year = "2023",
doi = "10.1109/ICoICT58202.2023.10262698",
language = "English",
series = "International Conference on ICT Convergence",
publisher = "IEEE Computer Society",
pages = "528--532",
booktitle = "2023 11th International Conference on Information and Communication Technology, ICoICT 2023",
address = "United States",
}