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Titlebook: Cross-Word Modeling for Arabic Speech Recognition; Dia AbuZeina,Moustafa Elshafei Book 2012 Dia AbuZeina 2012 Arabic linguistics.Arabic ph

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發(fā)表于 2025-3-21 18:18:23 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱Cross-Word Modeling for Arabic Speech Recognition
編輯Dia AbuZeina,Moustafa Elshafei
視頻videohttp://file.papertrans.cn/241/240379/240379.mp4
概述Contents and results will be presented with help of an up-and-running ASR system.Examines Arabic phonology, using an HHM-based classifier.Uses phonological rules to overcome what is known as the cross
叢書(shū)名稱SpringerBriefs in Speech Technology
圖書(shū)封面Titlebook: Cross-Word Modeling for Arabic Speech Recognition; Dia AbuZeina,Moustafa Elshafei Book 2012 Dia AbuZeina 2012 Arabic linguistics.Arabic ph
描述.Cross-Word Modeling for Arabic Speech Recognition. utilizes phonological rules in order to model the cross-word problem, a merging of adjacent words in speech caused by continuous speech, to enhance the performance of continuous speech recognition systems. The author aims to provide an understanding of the cross-word problem and how it can be avoided, specifically focusing on Arabic phonology using an HHM-based classifier.
出版日期Book 2012
關(guān)鍵詞Arabic linguistics; Arabic phonological rules; Arabic phonology; Continuous speech recognition systems;
版次1
doihttps://doi.org/10.1007/978-1-4614-1213-7
isbn_softcover978-1-4614-1212-0
isbn_ebook978-1-4614-1213-7Series ISSN 2191-737X Series E-ISSN 2191-7388
issn_series 2191-737X
copyrightDia AbuZeina 2012
The information of publication is updating

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沙發(fā)
發(fā)表于 2025-3-21 23:42:28 | 只看該作者
板凳
發(fā)表于 2025-3-22 01:22:38 | 只看該作者
Reconstructing Teachers of MathematicsThis chapter presents a brief overview of the evolution of Arabic speech recognition systems. It provides a literature survey of Arabic speech recognition systems and discusses some of the challenges of Arabic from the speech recognition point of view.
地板
發(fā)表于 2025-3-22 08:39:21 | 只看該作者
5#
發(fā)表于 2025-3-22 12:48:20 | 只看該作者
Reconstructing Teachers of MathematicsThis chapter discusses some Arabic phonological rules that can be used to capture the variations occurring at words’ junctures. The rules include Idgham, Iqlaab, Hamzat Al-Wasl, and merging of two consecutive unvoweled letters. Then, an algorithm to model this problem is provided.
6#
發(fā)表于 2025-3-22 14:40:19 | 只看該作者
Wetlands: A Multidisciplinary PerspectiveThis chapter presents the results achieved by modeling cross-word pronunciation variation problem of MSA. We practically investigated two MSA phonological rules (Idgham and Iqlaab) which significantly enhanced recognition accuracy. Three ASR’s metrics were measured: word error rate (WER), out of vocabulary (OOV), and perplexity (PP).
7#
發(fā)表于 2025-3-22 18:07:29 | 只看該作者
Arabic Speech Recognition Systems,This chapter presents a brief overview of the evolution of Arabic speech recognition systems. It provides a literature survey of Arabic speech recognition systems and discusses some of the challenges of Arabic from the speech recognition point of view.
8#
發(fā)表于 2025-3-22 22:10:29 | 只看該作者
Cross-Word Pronunciation Variations,This chapter presents the cross-word problem of the Arabic language. It also includes the main sources of this problem: Idgham (merging), Iqlaab (changing), Hamzat Al-Wasl deleting, and merging of two consecutive unvoweled letters. Illustrative examples of the cross-word problem are also provided.
9#
發(fā)表于 2025-3-23 04:51:54 | 只看該作者
Modeling of Arabic Cross-Word Pronunciation Variations,This chapter discusses some Arabic phonological rules that can be used to capture the variations occurring at words’ junctures. The rules include Idgham, Iqlaab, Hamzat Al-Wasl, and merging of two consecutive unvoweled letters. Then, an algorithm to model this problem is provided.
10#
發(fā)表于 2025-3-23 06:30:26 | 只看該作者
Performance and Evaluation,This chapter presents the results achieved by modeling cross-word pronunciation variation problem of MSA. We practically investigated two MSA phonological rules (Idgham and Iqlaab) which significantly enhanced recognition accuracy. Three ASR’s metrics were measured: word error rate (WER), out of vocabulary (OOV), and perplexity (PP).
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