Speech Enhancement Algorithms: A Systematic Literature Review
A growing and pressing need for Speech Enhancement Algorithms (SEAs) has emerged with the proliferation of hearing devices and mobile devices that aim to improve speech intelligibility without sacrificing speech quality. Recently, a tremendous number of studies have been conducted in the field of sp...
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MDPI AG
2025-05-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/18/5/272 |
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| author | Sally Taha Yousif Basheera M. Mahmmod |
| author_facet | Sally Taha Yousif Basheera M. Mahmmod |
| author_sort | Sally Taha Yousif |
| collection | DOAJ |
| description | A growing and pressing need for Speech Enhancement Algorithms (SEAs) has emerged with the proliferation of hearing devices and mobile devices that aim to improve speech intelligibility without sacrificing speech quality. Recently, a tremendous number of studies have been conducted in the field of speech enhancement. This study aims to map the field of speech enhancement by conducting a systematic literature review to provide comprehensive details of recently proposed SEAs. This systematic review aims to highlight research trends in SEAs and direct researchers to the most important topics published between 2015 and 2024. It attempts to address seven key research questions related to this topic. Moreover, it covers articles available in five research databases that were selected in accordance with the PRISMA protocol. Different inclusion and exclusion criteria have been performed. Across the selected databases, 47 studies met the defined inclusion criteria. A detailed explanation of SEAs in the recent literature is provided, with existing SEAs studied in a comparative fashion along with the factors influencing the choice of one over the others. This review presents different criteria related to the approaches utilized for signal modeling, the different datasets employed, types of transform-based SEAs, and the effectiveness of different measurements, among other topics. This study presents a systematic review of SEAs along with existing challenges in this field. |
| format | Article |
| id | doaj-art-f0a9f1bf8b3846bc850cecfe91fafaed |
| institution | DOAJ |
| issn | 1999-4893 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj-art-f0a9f1bf8b3846bc850cecfe91fafaed2025-08-20T03:14:38ZengMDPI AGAlgorithms1999-48932025-05-0118527210.3390/a18050272Speech Enhancement Algorithms: A Systematic Literature ReviewSally Taha Yousif0Basheera M. Mahmmod1Department of Computer Engineering, University of Baghdad, Al-Jadriya, Baghdad 10071, IraqDepartment of Computer Engineering, University of Baghdad, Al-Jadriya, Baghdad 10071, IraqA growing and pressing need for Speech Enhancement Algorithms (SEAs) has emerged with the proliferation of hearing devices and mobile devices that aim to improve speech intelligibility without sacrificing speech quality. Recently, a tremendous number of studies have been conducted in the field of speech enhancement. This study aims to map the field of speech enhancement by conducting a systematic literature review to provide comprehensive details of recently proposed SEAs. This systematic review aims to highlight research trends in SEAs and direct researchers to the most important topics published between 2015 and 2024. It attempts to address seven key research questions related to this topic. Moreover, it covers articles available in five research databases that were selected in accordance with the PRISMA protocol. Different inclusion and exclusion criteria have been performed. Across the selected databases, 47 studies met the defined inclusion criteria. A detailed explanation of SEAs in the recent literature is provided, with existing SEAs studied in a comparative fashion along with the factors influencing the choice of one over the others. This review presents different criteria related to the approaches utilized for signal modeling, the different datasets employed, types of transform-based SEAs, and the effectiveness of different measurements, among other topics. This study presents a systematic review of SEAs along with existing challenges in this field.https://www.mdpi.com/1999-4893/18/5/272speech enhancementWiener filterMMSEdeep learningnoisespeech signal |
| spellingShingle | Sally Taha Yousif Basheera M. Mahmmod Speech Enhancement Algorithms: A Systematic Literature Review Algorithms speech enhancement Wiener filter MMSE deep learning noise speech signal |
| title | Speech Enhancement Algorithms: A Systematic Literature Review |
| title_full | Speech Enhancement Algorithms: A Systematic Literature Review |
| title_fullStr | Speech Enhancement Algorithms: A Systematic Literature Review |
| title_full_unstemmed | Speech Enhancement Algorithms: A Systematic Literature Review |
| title_short | Speech Enhancement Algorithms: A Systematic Literature Review |
| title_sort | speech enhancement algorithms a systematic literature review |
| topic | speech enhancement Wiener filter MMSE deep learning noise speech signal |
| url | https://www.mdpi.com/1999-4893/18/5/272 |
| work_keys_str_mv | AT sallytahayousif speechenhancementalgorithmsasystematicliteraturereview AT basheerammahmmod speechenhancementalgorithmsasystematicliteraturereview |