Detect Multi Spoken Languages Using Bidirectional Long Short-Term Memory
Many speaker language detection systems depend on deep learning (DL) approaches, and utilize long recorded audio periods to achieve satisfactory accuracy. This study aims to extract features from short recording audio files that are convenient in order to detect the spoken languages under test succe...
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| Format: | Article |
| Language: | English |
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Mosul University
2023-06-01
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| Series: | Al-Rafidain Journal of Computer Sciences and Mathematics |
| Subjects: | |
| Online Access: | https://csmj.mosuljournals.com/article_179455_cdcd50baadc6189f87369d3af5c6e9f1.pdf |
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| author | Fawziya Ramo Mohammed Kannah |
| author_facet | Fawziya Ramo Mohammed Kannah |
| author_sort | Fawziya Ramo |
| collection | DOAJ |
| description | Many speaker language detection systems depend on deep learning (DL) approaches, and utilize long recorded audio periods to achieve satisfactory accuracy. This study aims to extract features from short recording audio files that are convenient in order to detect the spoken languages under test successfully. This detection process is based on audio files of (1 or 2) seconds whereas most of the previous languages Classification systems were based on much longer time frames (from 3 to 10 seconds). This research defined and implemented many low-level features using Mel Frequency Cepstral Coefficients (MFCCs), the dataset compiled by the researcher containing speech files in three languages (Arabic, English. Kurdish), which is called M2L_dataset is the source of data used in this paper.A Bidirectional Long Short-Term Memory (BiLSTM) algorithm applied in this paper for detection speaker language and the result was perfect, binary language detection had a test accuracy of 100%, and three languages detection had a test accuracy of 99.19%. |
| format | Article |
| id | doaj-art-8a503ed60cab454cb0c6eeb57e7bbf6b |
| institution | DOAJ |
| issn | 1815-4816 2311-7990 |
| language | English |
| publishDate | 2023-06-01 |
| publisher | Mosul University |
| record_format | Article |
| series | Al-Rafidain Journal of Computer Sciences and Mathematics |
| spelling | doaj-art-8a503ed60cab454cb0c6eeb57e7bbf6b2025-08-20T03:06:47ZengMosul UniversityAl-Rafidain Journal of Computer Sciences and Mathematics1815-48162311-79902023-06-011711810.33899/csmj.2023.179455179455Detect Multi Spoken Languages Using Bidirectional Long Short-Term MemoryFawziya Ramo0Mohammed Kannah1Department of Computer Science, College of computer science and mathematics, Mosul University, Mosul, IraqThe General Directorate of Education in Nineveh Governorate, Mosul, IraqMany speaker language detection systems depend on deep learning (DL) approaches, and utilize long recorded audio periods to achieve satisfactory accuracy. This study aims to extract features from short recording audio files that are convenient in order to detect the spoken languages under test successfully. This detection process is based on audio files of (1 or 2) seconds whereas most of the previous languages Classification systems were based on much longer time frames (from 3 to 10 seconds). This research defined and implemented many low-level features using Mel Frequency Cepstral Coefficients (MFCCs), the dataset compiled by the researcher containing speech files in three languages (Arabic, English. Kurdish), which is called M2L_dataset is the source of data used in this paper.A Bidirectional Long Short-Term Memory (BiLSTM) algorithm applied in this paper for detection speaker language and the result was perfect, binary language detection had a test accuracy of 100%, and three languages detection had a test accuracy of 99.19%.https://csmj.mosuljournals.com/article_179455_cdcd50baadc6189f87369d3af5c6e9f1.pdfdeep learningbidirectional long short-term memorymfccspeaker language detection |
| spellingShingle | Fawziya Ramo Mohammed Kannah Detect Multi Spoken Languages Using Bidirectional Long Short-Term Memory Al-Rafidain Journal of Computer Sciences and Mathematics deep learning bidirectional long short-term memory mfcc speaker language detection |
| title | Detect Multi Spoken Languages Using Bidirectional Long Short-Term Memory |
| title_full | Detect Multi Spoken Languages Using Bidirectional Long Short-Term Memory |
| title_fullStr | Detect Multi Spoken Languages Using Bidirectional Long Short-Term Memory |
| title_full_unstemmed | Detect Multi Spoken Languages Using Bidirectional Long Short-Term Memory |
| title_short | Detect Multi Spoken Languages Using Bidirectional Long Short-Term Memory |
| title_sort | detect multi spoken languages using bidirectional long short term memory |
| topic | deep learning bidirectional long short-term memory mfcc speaker language detection |
| url | https://csmj.mosuljournals.com/article_179455_cdcd50baadc6189f87369d3af5c6e9f1.pdf |
| work_keys_str_mv | AT fawziyaramo detectmultispokenlanguagesusingbidirectionallongshorttermmemory AT mohammedkannah detectmultispokenlanguagesusingbidirectionallongshorttermmemory |