Automatic Language Identification from Audio Signals using LSTM-RNN

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dc.contributor.authorBatir Sharimbaev
dc.contributor.authorShirali Kadyrov
dc.date.accessioned2025-11-13T06:30:14Z
dc.date.available2025-11-13T06:30:14Z
dc.date.issued2023
dc.description.abstractThe objective of this study is to develop an efficient Language Identification (LID) system using Long Short-Term Memory Recurrent Neural Networks applied to audio signals. Two experiments were conducted to validate the proposed approach. The experimental results demonstrated exceptional performance, with an accuracy of 98% and 97.6% on the test sets of the first and second experiments, respectively. The models were trained and tested using audio recordings in English, Russian, Turkish, Kyrgyz, and Kazakh languages. These findings suggest that the proposed LID system is highly effective and can be used in various real-world applications.
dc.identifier.citationBatir Sharimbaev, Shirali Kadyrov / Automatic Language Identification from Audio Signals using LSTM-RNN/ 17th International Conference on Electronics Computer and Computation (ICECCO) / 2023
dc.identifier.urihttps://repository.sdu.edu.kz/handle/123456789/2187
dc.language.isoen
dc.publisher17th International Conference on Electronics Computer and Computation (ICECCO)
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/
dc.subjectRNN
dc.subjectLSTM
dc.subjectLanguage Identification
dc.subjectAudio Signals
dc.titleAutomatic Language Identification from Audio Signals using LSTM-RNN
dc.typeArticle

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