Anti-stammering algorithm with adapted multi-layer perceptron
Stuttering (or stammering) is a common speech disorder that may continue until adulthood, if not treated in its early stages. In this study, we suggested an efficient algorithm to perform stammering corrections (anti-stammering). This algorithm includes an effective feature extraction approach and a...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Penerbit Universiti Kebangsaan Malaysia
2024
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| Online Access: | http://journalarticle.ukm.my/25732/ http://journalarticle.ukm.my/25732/1/12.pdf |
| _version_ | 1848816435781959680 |
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| author | Ali Bashar Hussein, Al-Nima, Raid Rafi Omar Han, Tingting |
| author_facet | Ali Bashar Hussein, Al-Nima, Raid Rafi Omar Han, Tingting |
| author_sort | Ali Bashar Hussein, |
| building | UKM Institutional Repository |
| collection | Online Access |
| description | Stuttering (or stammering) is a common speech disorder that may continue until adulthood, if not treated in its early stages. In this study, we suggested an efficient algorithm to perform stammering corrections (anti-stammering). This algorithm includes an effective feature extraction approach and an adapted classifier. We introduced Enhanced 1D Local Binary Patterns (EOLBP) for the extraction of features and adapted a classifier of Multi-Layer Perceptron (MLP) neural network for regression. This paper uses a database that involves speech signals with stammering, it can be called the Fluency Bank (FB). The result reveals that the proposed anti-stammering algorithm obtains promising achievement, where a high accuracy of 97.22% is attained. |
| first_indexed | 2025-11-15T01:05:50Z |
| format | Article |
| id | oai:generic.eprints.org:25732 |
| institution | Universiti Kebangasaan Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T01:05:50Z |
| publishDate | 2024 |
| publisher | Penerbit Universiti Kebangsaan Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:generic.eprints.org:257322025-08-12T01:44:43Z http://journalarticle.ukm.my/25732/ Anti-stammering algorithm with adapted multi-layer perceptron Ali Bashar Hussein, Al-Nima, Raid Rafi Omar Han, Tingting Stuttering (or stammering) is a common speech disorder that may continue until adulthood, if not treated in its early stages. In this study, we suggested an efficient algorithm to perform stammering corrections (anti-stammering). This algorithm includes an effective feature extraction approach and an adapted classifier. We introduced Enhanced 1D Local Binary Patterns (EOLBP) for the extraction of features and adapted a classifier of Multi-Layer Perceptron (MLP) neural network for regression. This paper uses a database that involves speech signals with stammering, it can be called the Fluency Bank (FB). The result reveals that the proposed anti-stammering algorithm obtains promising achievement, where a high accuracy of 97.22% is attained. Penerbit Universiti Kebangsaan Malaysia 2024-09 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/25732/1/12.pdf Ali Bashar Hussein, and Al-Nima, Raid Rafi Omar and Han, Tingting (2024) Anti-stammering algorithm with adapted multi-layer perceptron. Jurnal Kejuruteraan, 36 (5). pp. 1921-1933. ISSN 0128-0198 https://www.ukm.my/jkukm/volume-3605-2024/ |
| spellingShingle | Ali Bashar Hussein, Al-Nima, Raid Rafi Omar Han, Tingting Anti-stammering algorithm with adapted multi-layer perceptron |
| title | Anti-stammering algorithm with adapted multi-layer perceptron |
| title_full | Anti-stammering algorithm with adapted multi-layer perceptron |
| title_fullStr | Anti-stammering algorithm with adapted multi-layer perceptron |
| title_full_unstemmed | Anti-stammering algorithm with adapted multi-layer perceptron |
| title_short | Anti-stammering algorithm with adapted multi-layer perceptron |
| title_sort | anti-stammering algorithm with adapted multi-layer perceptron |
| url | http://journalarticle.ukm.my/25732/ http://journalarticle.ukm.my/25732/ http://journalarticle.ukm.my/25732/1/12.pdf |