Arabic Discourse Analysis: A Naïve Algorithm for Defective Pronunciation Correction

Terbeh, Naim; Zrigui, Mounir

doi:10.13053/cys-23-1-2820

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…The second part is to extract the key points of the lips. Since dynamic lip information is more difficult to extract accurately than static one, the critical issues of the angular features of the lips are mainly extracted here [7]. The third part is to fuse the video key points with the speech and compare the processed audio and video with the pre-recorded standard pronunciation video.…”

Section: Lip Angle Fusion Model Figure 2 Lip Angle Fusion Modelmentioning

confidence: 99%

Application of Speech Recognition in English PronunciationCorrection

Zhang¹

2023

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In the context of economic globalization, countries are beginning to conduct ordinary trade transactions, so English, asthe most frequently used language, has been incorporated into the education system of each government and studentsare being taught English systematically, but the fastest way to improve English must be for a student to have a one-onone lesson with a native English speaker. However, with the world’s advanced educational resources, teaching Englishto students on a one-to-one basis is obviously not practical. Therefore, implanting a speech recognition system intoeducational software to help the software track or correct students’ English learning in real time is possible. However,replacing a real English teacher with the current speech recognition accuracy rate is still a challenge. Therefore, thispaper explains in detail how to improve speech recognition accuracy through lip motion tracking and MFCC and to givelearners the corresponding vocabulary ratings and suggestions for improvement through this model. In the long run, theknowledge tracking model is used to help students find the most suitable learning style.

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Section: Lip Angle Fusion Model Figure 2 Lip Angle Fusion Modelmentioning

confidence: 99%

Application of Speech Recognition in English PronunciationCorrection

Zhang¹

2023

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“…It is a Semitic and official language of the Arab world (Zrigui et al 2016). It has more than 445 million speakers and ranks eighth among the number of pages on the Internet (Mahmoud and Zrigui 2020;Terbeh et al 2019). Its automatic processing is difficult and requires a lot of time because of its great variability of specificities (Meddeb et al 2016):…”

Section: Issues Within Smart Campusesmentioning

confidence: 99%

Personalized Smart Learning Recommendation System for Arabic Users in Smart Campus

Meddeb

Zrigui

2021

International Journal of Web-Based Learning and Teaching Technologies

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The advancement of technologies has modernized learning within smart campuses and has emerged new context through communication between mobile devices. Although there is a revolutionary way to deliver long-term education, a great diversity of learners may have different levels of expertise and cannot be treated in a consistent manner. Nevertheless, multimedia documents recommendation in Arabic language has represented a problem in Natural Language Processing (NLP) due to their richness of features and analysis ambiguities. To tackle the sparsity problem, smart learning recommendation-based approach is proposed for inferring the format of the suitable Arabic document in a contextual situation. Indeed, the user-document interactions are modeled efficiently through deep neural networks architectures. Given the contextual sensor data, the suitable document with the best format is thereafter predicted. The findings suggest that the proposed approach might be effective in improving the learning quality and the collaboration notion in smart learning environment

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“…As a result, it becomes vital to create new tools to aid in the comprehension of flawed Arabic discourses. Speech that is illogical will then be automatically corrected [11]. In Arabic, there are four divisions: Model Standard Arabic (MSA), which is used in newspapers, books, broadcasts, documentaries, formal situations, or when the reader or audience is from a different nation of Arabic, and Dialectal Arabic (DA), which is a dialect of everyday experiences, are all still in use today.…”

Section: Introductionmentioning

confidence: 99%

Arabic Dialogue Processing and Act Classification using Support Vector Machine

Alsubayhay¹,

Salam²,

Mohamed³

2023

IJACSA

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Text classification is the technique of grouping documents according to their content into classes and groups. As a result of the vast amount of textual material available online, this procedure is becoming increasingly crucial. The primary challenge in text categorization is enhancing classification accuracy. This role is receiving more attention due to its importance in the development of these systems and the categorization of Arabic dialogue processing. In the research, attempts were made to define dialogue processing. It concentrates on classifying words that are used in dialogue. There are various types of dialogue processing, including hello, farewell, thank you, confirm, and apologies. The words are used in the study without context. The proposed approach recovers the properties of function words by replacing collocations with standard number tokens and each substantive keyword with a numerical approximation token. With the use of the linear support vector machine (SVM) technique, the classification method for this study was obtained. The act is classified using the linear SVM technique, and the anticipated accuracy is evaluated against that of alternative algorithms. This study encompasses Arabic dialogue acts corpora, annotation schema, and classification problems. It describes the outcomes of contemporary approaches to classifying Arabic dialogue acts. A custom database in the domains of banks, chat, and airline tickets is used in the research to assess the effectiveness of the suggested solutions. The linear SVM approach produced the best results.

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Arabic Discourse Analysis: A Naïve Algorithm for Defective Pronunciation Correction

Cited by 5 publications

References 13 publications

Application of Speech Recognition in English PronunciationCorrection

Application of Speech Recognition in English PronunciationCorrection

Personalized Smart Learning Recommendation System for Arabic Users in Smart Campus

Arabic Dialogue Processing and Act Classification using Support Vector Machine

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