Arabic Utterances Research Articles

2D Spectrogram analysis using vision transformer to detect mispronounced Arabic utterances for children

Pronunciation feedback is essential for teaching languages to children, urging the need to create computer-assisted pronunciation training (CAPT) systems to automate this process. Most of the current CAPT systems for Arabic Language examined a wide age range, including children, with a dominant adults’ portion. However, these systems did not consider many challenges facing children’s voice, in terms of the acoustic difference between children and adults. Moreover, due to the lack of publicly Arabic datasets, Arabic-related systems were evaluated on datasets consisting of the sound of Arabic letters, with minor efforts to contain words or sentences. In this paper, we propose the Arabic Utterance Mispronunciation Detector for Children (AUMD-Child) system that detects the mispronunciation of children for Arabic words at real-time. The proposed system is based on the fusion of Vision Transformer (ViT) and transfer learning-based model (AlexNet). A novel public dataset is constructed, in which 16 words have been collected from children aged between 7 and 12 years old to evaluate our proposed system. The experimental results, compared to the state-of-the-art models in literature, showed that the accuracy of our proposed AUMD-Child system exceeded the handcrafted features-based models by an average of 7 %, the CNN features-based models using AlexNet and SVM by 5 %, transfer learning-based model using AlexNet by 4 %, and transformers-based model using ViT by 2 %, achieving an average accuracy of 91.81 % for detecting the mispronunciation patterns in Arabic words but in an average processing time of 33 ms, exceeding the other state-of-the-art models with 13 %, 0.03 %, 0.06 %, and 23 % respectively.

Evaluating Translation Tools: Google Translate, Bing Translator, and Bing AI on Arabic Colloquialisms

This study examines the advancements in AI-driven machine translation, specifically focusing on the accurate translation of Arabic colloquial expressions. It aims to assess the progress made by Large Language Models, such as Bing AI Chat, compared to traditional machine translation systems. By focusing on colloquial expressions, this research aims to shed light on the challenges and opportunities for improvement in machine translation systems, particularly when dealing with the complexities of translating informal Arabic utterances. Building upon At-tall’s 2019 thesis, which compared Google Translate and human translators, the study employs the same Arabic sentences as a test dataset, allowing for a direct comparison between 2019 translations and those produced by current machine translation tools. The findings indicate limited improvement in Google Translate since 2019, with Bing Translator exhibiting a similar level of translation accuracy. In contrast, Bing AI Chat consistently outperformed the other systems, showcasing the potential of Large Language Model machine translation. Notably, Bing AI Chat provided interpretations and valuable comments on the tested Arabic phrases, demonstrating a deeper understanding of the intended meaning. This study contributes significantly to the field of machine translation by providing evidence of the potential of Large Language Model systems in producing more accurate Arabic-English translations. It emphasizes the advantage of Large Language Models in dealing with non-standard Arabic expressions, encouraging further exploration of Large Language Model-powered approaches in machine translation. The findings offer a promising pathway towards achieving more accurate and expressive translations across diverse languages and cultures.

The Relevance and Application of Structuralist Linguistics in Arabic Language Learning

Structuralist theory is one of the important theories in language learning. This theory raises fundamental questions regarding its relevance to language, especially second language. This study aims to describe the view of structuralist theory and its application in Arabic language learning. This research is a conceptual study using descriptive-qualitative method. The results show that some learning models that can be applied based on structuralist views are; 1) repetitive practice of Arabic utterances that can have an impact on students' habituation in speaking Arabic, 2) changing the type of sentence between isim or fi'il, 3) Expansion of meaning exercises in sentences with different patterns, and 4) linking exercises.

Arabic Metonymy and Synecdoche in English Translation: The Case of Body Parts

This paper examines the figurative use, both metonymy and synecdoche, of Arabic body parts in English literary translation. The aim is twofold: first to investigate the translational procedures used to render them and, second, to see what linguistic and cultural constraints determine translational choices. The corpus consists of first-encountered 100 Arabic utterances featuring names of body parts extracted from Naguib Mahfouz’s (1947) novel زقاق المدق and their English renditions in LeGassick’s translation Zuqaq Almadaq (1992). The data is first categorized in terms of literal and figurative use, and then the latter is classified into metonymy and synecdoche. The results show that translational choices are governed by linguistic, aesthetic, and cultural constraints of both English and Arabic discourse. The study concludes that reference to body parts varies in terms of frequency in favor of Arabic but they are a common feature of both languages’ literary discourse. Therefore, the translator has to be sensitive to their literal as well as figurative meaning within each culture, in order to provide, when possible, a parallel physical description and preserve the aesthetic value of metonymic and synecdochic expressions. Keywords: Arabic, English, Body parts, Metonymy, Synecdoche, Translation.

Sentiment Analysis in Poems in Misurata Sub-dialect

Over the recent decades, there has been a significant increase and development of resources for Arabic natural language processing. This includes the task of exploring Arabic Language Sentiment Analysis (ALSA) from Arabic utterances in both Modern Standard Arabic (MSA) and different Arabic dialects. This study focuses on detecting sentiment in poems written in Misurata Arabic sub-dialect spoken in Misurata, Libya. The tools used to detect sentiment from the dataset are Sklearn as well as Mazajak sentiment tool1. Logistic Regression, Random Forest, Naive Bayes (NB), and Support Vector Machines (SVM) classifiers are used with Sklearn, while the Convolutional Neural Network (CNN) is implemented with Mazajak. The results show that the traditional classifiers score a higher level of accuracy as compared to Mazajak which is built on an algorithm that includes deep learning techniques. More research is suggested to analyze Arabic sub-dialect poetry in order to investigate the aspects that contribute to sentiments in these multi-line texts; for example, the use of figurative language such as metaphors.

Arabic utterances in a multilingual world: Shāh Walī-Allāh and Qur’anic translatability in North India

ABSTRACT This article investigates how Arabic texts reached multilingual audiences in North India in the eighteenth century. Drawing on a remarkable treatise on translating the Qur’an into Persian by the Delhi intellectual Shāh Walī-Allāh (d. 1176/1762), it argues that so-called “interlinear” translations functioned to preserve the sound of Arabic utterances as well as their meaning. By anchoring another language to Arabic utterances, these translations also reified symbolic hierarchies between Arabic and languages used to translate it. Walī-Allāh’s understanding of translatability was closely tied up with notions of Qur’anic structure (naẓm) and the sonic qualities of the Arabic language, but also informed by a sensitivity to linguistic difference in the multilingual society he lived in.

Classical Arabic words memorized by the Algerian colloquial struggle for survival

It is not possible for us to enumerate all the classical Arabic utterances that the Algerian colloquial memorized, as they are many and varied. It is found among the folds of the old Arabic dictionaries, and any reader or student can refer to it to investigate and find out his creativity from it. Through the study that we sought to carry out, and after investigating these terms and their meanings, we reached the following results: 1- The Arabic language had a great influence on others, and it was rooted and rooted. 2- Most of the Arabic dialects contain eloquent words, whether they convey the same meaning or contradict it. It has been shown that their derivation has not changed over the years and ages. .

Pragmatic failure in translating Arabic implicatures into English

The aim of this paper is twofold. First, it attempts to demonstrate that Arabic utterances involving euphemisms, tautologies and ironies (henceforth Arabic implicatures) lend themselves readily to a Gricean interpretation and, second, it shows how Arabic implicatures in their immediate, social context of use exhibit pragmatic failures when rendered into English. The study examines and analyzes ten Arabic utterances involving implicatures in their original contexts of situation taken from Mahfouz’s (1947)Ziqāqal-Midaqwhich was translated by LeGassick (1966) into ‘Midaq Alley’, and Ṭayib Ṣaleḥ’s (1966)Mawsimu al-Hijra ila ashShamāl, which was rendered by Davies (1969) into ‘the Season of Migration to the North’. The study argues that to avoid pragmatic failure when translating Arabic implicatures into English, emphasis should be placed on conveying the pragmatic import of these utterances by the employment of various translation strategies ranging from those capturing the form and/or function to those capturing the communicative sense independently.

Effect of speech variable rate on the coarticulation in the right vocalic context of Arabic utterances VCV

Our study consists of analysing Arabic utterances VCVα in brief vocalic context with Vα and speech rate as variables in order to observe the impact of the “right” context and speech rate on the coarticulation. Thus, we have to look for some invariance in the speech signal explaining the coarticulation phenomenon related to speech rate. So, we have analyzed the formant tracking of the arabic pharyngeal / / (or (ɛ) in arabic) in vocalises contexts /a/, /u/ and /i/ with variables speech rates (normal, fast and slow) in interrogative sentences. That is in order to confirm the anticipatory phenomenon and observed the influence of speech rate variation on the vocal tract in articulation. The observed results have shown in our case the existence of anticipatory articulation and that it depends on speech rate. (Non existence in slow rate, however, with more prominence in normal or fast rate.)

Realistic Face and Lip Expressions for a Bilingual Humanoid Robot

Abstract Hala is a bi-lingual (Arabic and English) robot receptionist located at Carnegie Mellon in Qatar. Hala is presented to users as a 3-D animated face on a screen. Users type to her, and she replies in speech synced with realistic facial expressions. However there are two existing problems with the robot. First, Hala's animation engine does not fully adhere to existing research on face dynamics, which makes it difficult to create natural and interesting facial expressions. Natural expressions help towards an engaging user experience by articulating non-verbal aspects (ex: confusion, glee, horror, etc). Second, while speaking in Arabic lip-movements are not realistic because they were adopted from English utterances. In this work we address these two limitations. Similar to the movie and video-game industry, we leverage Paul Ekman's seminal work on Facial Action Coding System (FACS) to demarcate Hala's 3D face model into muscle-primitives. These primitives are used to compose complex, yet natural, facial expressions. We have also authored an in-house tool, which allows non-programmers (for ex: artists) to manipulate the face in real-time to create expressions. The sounds humans make while talking are symbolically captured as “phonemes”. The corresponding shapes of the lips, for these sounds (i.e. phonemes), are called “visemes”. We used existing research and observed each other (and a mirror), to develop visemes that accurately capture Arabic pronunciations. Hala can thus utilize English and Arabic visemes for accurate lip-movement and syncing. We empirically tested and evaluated our work by comparing it with previous lip movements for common Arabic utterances. Nonetheless, certain pronunciations can fire less-than-ideal visemes if they are preceded by silence. Upon identifying and addressing these limitations, Hala has 11 new facial expressions for a more natural looking and behaving robot. This work also pioneered the first implemented subset of Arabic visemes on a robot.