Enhancing Arabic text-to-speech synthesis for emotional expression in visually impaired individuals using the artificial hummingbird and hybrid deep learning model
Depression is one of the most dangerous mental health conditions, often leading to suicide, which is the fourth leading cause of death in the Middle East. Particularly, Egypt has the highest suicide rate in the region, making it crucial to recognize depression and suicidal thoughts early. In Arab cu...
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Elsevier
2025-04-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016825001784 |
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| author | Mahmoud M. Selim Mohammed S. Assiri |
| author_facet | Mahmoud M. Selim Mohammed S. Assiri |
| author_sort | Mahmoud M. Selim |
| collection | DOAJ |
| description | Depression is one of the most dangerous mental health conditions, often leading to suicide, which is the fourth leading cause of death in the Middle East. Particularly, Egypt has the highest suicide rate in the region, making it crucial to recognize depression and suicidal thoughts early. In Arab culture, awareness of mental health issues is limited, but in recent years, people have increasingly expressed their feelings on social media platforms. This shift presents an opportunity for mental health intervention through digital means. Furthermore, while facial expressions are not accessible to the blind and visually impaired, voice signals can convey emotional nuances, offering an alternative method for detecting mental health states. Natural Language Processing (NLP) and machine learning (ML) techniques provide powerful tools for analysing social media text data, helping detect emotional distress and providing timely support. By applying these technologies, AI-driven solutions can assist in understanding and addressing mental health concerns more inclusively. This study designs an Arabic Mood Changing and Depression Detection using the Artificial Hummingbird Optimization Algorithm with Deep Learning (AMCDD-AHODL) technique for visually impaired individuals. The AMCDD-AHODL technique detects different kinds of emotions and depression using Arabic tweets. After pre-processing, the word embedding process is carried out using the AraBERT model. Furthermore, the AMCDD-AHODL technique utilizes a hybrid LSTM+BiGRU model for the recognition and classification model. To improve the performance of the hybrid LSTM+BiGRU methodology, the AMCDD-AHODL technique comprises an AHO-based hyperparameter tuning process. Finally, the WaveNet model enhances the naturalness and clarity of text-to-speech synthesis, delivering high-quality, human-like audio output. The AMCDD-AHODL approach is examined using the Modern Standard Arabic dataset containing 1229 records. The performance validation of the AMCDD-AHODL approach portrayed a superior accuracy value of 95.80 % compared to the existing ML and DL models. Therefore, the AMCDD-AHODL technique is applied for the early identification of various kinds of depression that can decrease the distress from the illness and the stigma related to mental health problems. |
| format | Article |
| id | doaj-art-778b271cb3c04d04be90a0f389e3edff |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-778b271cb3c04d04be90a0f389e3edff2025-02-10T04:34:14ZengElsevierAlexandria Engineering Journal1110-01682025-04-01119493502Enhancing Arabic text-to-speech synthesis for emotional expression in visually impaired individuals using the artificial hummingbird and hybrid deep learning modelMahmoud M. Selim0Mohammed S. Assiri1Department of Mathematics, College of Science and Humanities, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia; King Salman Center for Disability Research, Riyadh 11614, Saudi Arabia; Corresponding author at: Department of Mathematics, College of Science and Humanities, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia.Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, P.O. BOX 16273, Al-Kharj ZIP 3963, Saudi Arabia; King Salman Center for Disability Research, Riyadh 11614, Saudi ArabiaDepression is one of the most dangerous mental health conditions, often leading to suicide, which is the fourth leading cause of death in the Middle East. Particularly, Egypt has the highest suicide rate in the region, making it crucial to recognize depression and suicidal thoughts early. In Arab culture, awareness of mental health issues is limited, but in recent years, people have increasingly expressed their feelings on social media platforms. This shift presents an opportunity for mental health intervention through digital means. Furthermore, while facial expressions are not accessible to the blind and visually impaired, voice signals can convey emotional nuances, offering an alternative method for detecting mental health states. Natural Language Processing (NLP) and machine learning (ML) techniques provide powerful tools for analysing social media text data, helping detect emotional distress and providing timely support. By applying these technologies, AI-driven solutions can assist in understanding and addressing mental health concerns more inclusively. This study designs an Arabic Mood Changing and Depression Detection using the Artificial Hummingbird Optimization Algorithm with Deep Learning (AMCDD-AHODL) technique for visually impaired individuals. The AMCDD-AHODL technique detects different kinds of emotions and depression using Arabic tweets. After pre-processing, the word embedding process is carried out using the AraBERT model. Furthermore, the AMCDD-AHODL technique utilizes a hybrid LSTM+BiGRU model for the recognition and classification model. To improve the performance of the hybrid LSTM+BiGRU methodology, the AMCDD-AHODL technique comprises an AHO-based hyperparameter tuning process. Finally, the WaveNet model enhances the naturalness and clarity of text-to-speech synthesis, delivering high-quality, human-like audio output. The AMCDD-AHODL approach is examined using the Modern Standard Arabic dataset containing 1229 records. The performance validation of the AMCDD-AHODL approach portrayed a superior accuracy value of 95.80 % compared to the existing ML and DL models. Therefore, the AMCDD-AHODL technique is applied for the early identification of various kinds of depression that can decrease the distress from the illness and the stigma related to mental health problems.http://www.sciencedirect.com/science/article/pii/S1110016825001784Arabic languageNatural language processingDepression DetectionArtificial Hummingbird OptimizationVisually Impaired IndividualsText-to-Speech Synthesizer |
| spellingShingle | Mahmoud M. Selim Mohammed S. Assiri Enhancing Arabic text-to-speech synthesis for emotional expression in visually impaired individuals using the artificial hummingbird and hybrid deep learning model Alexandria Engineering Journal Arabic language Natural language processing Depression Detection Artificial Hummingbird Optimization Visually Impaired Individuals Text-to-Speech Synthesizer |
| title | Enhancing Arabic text-to-speech synthesis for emotional expression in visually impaired individuals using the artificial hummingbird and hybrid deep learning model |
| title_full | Enhancing Arabic text-to-speech synthesis for emotional expression in visually impaired individuals using the artificial hummingbird and hybrid deep learning model |
| title_fullStr | Enhancing Arabic text-to-speech synthesis for emotional expression in visually impaired individuals using the artificial hummingbird and hybrid deep learning model |
| title_full_unstemmed | Enhancing Arabic text-to-speech synthesis for emotional expression in visually impaired individuals using the artificial hummingbird and hybrid deep learning model |
| title_short | Enhancing Arabic text-to-speech synthesis for emotional expression in visually impaired individuals using the artificial hummingbird and hybrid deep learning model |
| title_sort | enhancing arabic text to speech synthesis for emotional expression in visually impaired individuals using the artificial hummingbird and hybrid deep learning model |
| topic | Arabic language Natural language processing Depression Detection Artificial Hummingbird Optimization Visually Impaired Individuals Text-to-Speech Synthesizer |
| url | http://www.sciencedirect.com/science/article/pii/S1110016825001784 |
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