Automatic Language Identification from Audio Signals using LSTM-RNN
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Date
2023
Authors
Batir Sharimbaev
Shirali Kadyrov
Journal Title
Journal ISSN
Volume Title
Publisher
17th International Conference on Electronics Computer and Computation (ICECCO)
Abstract
The 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.
Description
Keywords
RNN, LSTM, Language Identification, Audio Signals
Citation
Batir Sharimbaev, Shirali Kadyrov / Automatic Language Identification from Audio Signals using LSTM-RNN/ 17th International Conference on Electronics Computer and Computation (ICECCO) / 2023