Arabic Voice Research Articles

The Newcastle Laryngeal Hypersensitivity Questionnaire: Translation and Cross-Cultural Adaptation to the Arabic Language.

This study aims to validate and assess the reliability of the Arabic version of the Newcastle Laryngeal Hypersensitivity Questionnaire (LHQ) as a self-reported questionnaire for patients with laryngeal hypersensitivity syndrome (LHS). A prospective, observational, cross-sectional study was conducted in the ear, nose and throat Department of Kafr Elsheikh University Hospitals. The preliminary Arabic version of the LHQ (LHQ-Ar) was created using a rigorous forward and backward translation method. Native Arabic-speaking patients visiting outpatient pulmonology, speech pathology, and ear, nose and throat clinics in this tertiary referral center were invited to participate. Participants were divided into two groups: group 1, comprising patients with LHS, and group 2, consisting of healthy controls. All participants completed the LHQ-Ar questionnaire twice, with a two-week interval, to assess test-retest reliability. Additionally, LHS patients completed the Arabic Voice Handicap Index-10 to examine correlations with LHQ-Ar scores. The LHQ-Ar underwent rigorous testing for internal consistency, reliability, validity, sensitivity, and specificity to establish it as a validated patient questionnaire. The KMO test (KMO=0.955) and Bartlett's test (P<0.001) confirmed the sample's adequacy. The analysis identified only one factor with an eigenvalue greater than one, which explained 63.4% of the total variance. Items 13 (0.372) and 14 (0.306), which had commonalities below 0.5, were excluded. The final model, consisting of 12 items, accounted for 68.7% of the variance. Cronbach's alpha coefficients ranged from 0.933 to 0.988 for individual items and 0.995 for the total score, indicating excellent internal consistency. The Arabic version of the Newcastle LHQ (LHQ-Ar) proves to be a reliable and valid tool for assessing LHS in Arabic-speaking populations. The LHQ-Ar shows strong internal consistency, test-retest reliability, and good sensitivity and specificity in predicting LHS, thus making it a valuable instrument for clinical use and research in Arabic-speaking patients.

Optimized early fusion of handcrafted and deep learning descriptors for voice pathology detection and classification

This study presents an automated noninvasive voice disorder detection and classification approach using an optimized fusion of modified glottal source estimation and deep transfer learning neural network descriptors. A new set of modified descriptors based on a glottal source estimator and pre-trained Inception-ResNet-v2 convolutional neural network-based features are proposed for the speech disorder detection and classification task. The modified feature set is obtained using mel-cepstral coefficients, harmonic model, phase discrimination means, distortion deviation descriptors, conventional wavelet, and glottal source estimation features. Early descriptor-level fusion is employed in this study for performance enhancement-however, the fusion results in higher feature vector dimensionality. A nature-inspired slime mould algorithm is utilized to remove redundant and select the best discriminating features. Finally, the classification is performed using the K-nearest neighbor (KNN) classifier. The proposed algorithm was evaluated using extensive experiments with different feature combinations, with and without feature selection, and with two popular datasets: the Arabic Voice Pathology Database (AVPD) and the Saarbrucken Voice Database (SVD). We show that the proposed optimized fusion method attained an enhanced voice pathology detection accuracy of 98.46%, encompassing a wide spectrum of voice disorders on the SVD database. Furthermore, compared to traditional handcrafted and deep neural network-based techniques, the proposed method demonstrates competitive performance with fewer features.

Van Dijk’s ideological square in sourcing: was the Arab silenced or quoted in the Western media’s coverage of the 2011 revolution in Egypt?

This paper examines the Western media's coverage of the 2011 revolution in Egypt and its sourcing patterns using Van Dijk's ideological square framework. The study implements the research tools provided by Critical Discourse Analysis (CDA) to investigate the representation of Arab voices and analyzes whether they were silenced or adequately quoted. The findings reveal that the selected media outlets perpetuated power imbalances and social inequalities through their sourcing practices. The media over-quoted statements that supported Western narratives and marginalized or excluded statements that challenged these narratives or portrayed positive aspects of Arab culture and religion. This bias in sourcing patterns reflects a reproduction of dominant ideologies and reinforces the classical Orientalist discourse that positions Islam and Islamists as inferior entities. Furthermore, the study highlights the creation of dichotomous divisions between the Westerner and the Arab. The Westerner is portrayed as powerful and authoritative, while the Arab is depicted as subservient and in need of instruction.

Towards personalized control of things using Arabic voice commands for elderly and with disabilities people

Towards personalized control of things using Arabic voice commands for elderly and with disabilities people

Building Python Application for Webmail Interfaces Navigation using Voice Recognition Technology

Voice Recognition Technology (VRT) has played a crucial role in technology development, finding extensive use in the development of humanitarian assistance applications, including assistance programs for individuals with disabilities to use smart vehicles and smart homes, as well as websites. This paper discusses implementing a Computer Application (PC-App) for humanitarian assistance written in Python to enable Arabic-speaking elderly and handicapped employees to access and navigate webmail accounts using Arabic Voice Commands (AVC). Furthermore, a survey was conducted for elderly and disabled employees to assess the effectiveness of the application, with participants evaluating that it was useful in addition to improving their interaction with their accounts in Webmail. Ultimately, this application promotes independence and functionality for Arabic-speaking individuals, regardless of their mobility disability levels, by allowing them to independently use the Webmail interface using AVC.

Error Analysis of Errors in Arabic Phonetics at the University

Ma'had prepares the mustawa robi' class to produce female students who like to speak Arabic and can speak Arabic correctly, so that they can graduate with mumtaz predicate and can practice the knowledge they got when studying at Mahad Umar bin Khattab. The most important thing in learning Arabic is in learning listening skills where students listen to the teacher in explaining a title using Arabic sounds, and female students will present the title that has been explained in front of their friends using good and correct Arabic sounds. So the objectives of this study are: 1) to find out the Arabic sound errors. 2) to find a solution to the problems contained in the Arabic sound error. This research uses a qualitative approach with a descriptive type of research. The data collection techniques in this research by observations, interviews and documentation. The data analysis technique used in this research is Miles and huberman data analysis technique, as: The data reduction, data presentation and conclusion drawing. The results showed that: 1) mispronunciations of words, both in terms of phonetics, grammar and morphology. 2) Problem solving in Arabic voice is through continuous practice of speaking in Arabic voice, preparation before explaining the subject, and motive to be more confident.

PROSPECTIVE ARABIC LANGUAGE DATA BASE FOR IDENTIFICATION AND RECOGNITION OF VOICE DISORDER

&#x0D; &#x0D; &#x0D; Speech is the underlying strong characteristic and voice is the vibration that the air creates as it's pushed out of the lungs and going into the vocal cords. Vocal cords are two folds of tissue within the larynx, sometimes called a speech box, the sound of these strings is what gives rise to speech.Anything that interferes with vocal cord movement or contact can cause a voice disorder. Speech production may often be compromised as social stressors contribute to chronic aphonia or dysphonia.Machine learning algorithm and non-invasive systems may play a major role in the early detection and in tracking and even development of efficient pathological speech diagnosis, based on a computerized acoustic analysis. A strong voice impairment dataset will help in recognition and classification ofincreasing number of voice disorders in and outside the Arab area. The database protocols for AVPD (Arabic voice pathology database) has been developed in a way to prevent previous data base deficiencies in widely used databases like MEEI (Massachusetts eye and ear infirmary) and SVD (Saarbrücken voice database).&#x0D; &#x0D; &#x0D;

A review on voice pathology: Taxonomy, diagnosis, medical procedures and detection techniques, open challenges, limitations, and recommendations for future directions

Abstract Speech is a primary means of human communication and one of the most basic features of human conduct. Voice is an important part of its subsystems. A speech disorder is a condition that affects the ability of a person to speak normally, which occasionally results in voice impairment with psychological and emotional consequences. Early detection of voice problems is a crucial factor. Computer-based procedures are less costly and easier to administer for such purposes than traditional methods. This study highlights the following issues: recent studies, methods of voice pathology detection, machine learning and deep learning (DL) methods used in data classification, main datasets utilized, and the role of Internet of things (IoT) systems employed in voice pathology diagnosis. Moreover, this study presents different applications, open challenges, and recommendations for future directions of IoT systems and artificial intelligence (AI) approaches in the voice pathology diagnosis. Finally, this study highlights some limitations of voice pathology datasets in comparison with the role of IoT in the healthcare sector, which shows the urgent need to provide efficient approaches and easy and ideal medical diagnostic procedures and treatments of disease identification for doctors and patients. This review covered voice pathology taxonomy, detection techniques, open challenges, limitations, and recommendations for future directions to provide a clear background for doctors and patients. Standard databases, including the Massachusetts Eye and Ear Infirmary, Saarbruecken Voice Database, and the Arabic Voice Pathology Database, were used in most articles reviewed in this article. The classes, features, and main purpose for voice pathology identification are also highlighted. This study focuses on the extraction of voice pathology features, especially speech analysis, extends feature vectors comprising static and dynamic features, and converts these extended feature vectors into solid vectors before passing them to the recognizer.

Validation and Cultural Adaptation of the Arabic Version of the Aging Voice Index

The aging voice index (AVI) is a 23-item self-administered, patient-reported outcome measure. It was developed in the English language to assess the impact of voice disorders on the elderly population. This study aimed to develop an Arabic version of the AVI (A-AVI), test its reliability and validity, and assess its psychometric aspects in Arabic-speaking elderly persons with voice disorders. This was an observational, cross-sectional study involving elderly patients aged ≥60 years. Eighty-two patients with voice disorders were included in the dysphonia group and 77 patients without voice disorders were included in the vocally healthy group. The translated A-AVI and Arabic voice handicap index 10 (A-VHI10) were distributed to the study groups. The A-AVI was tested for its reliability (test-retest reliability and internal consistency) and validity (content, construct, and concurrent with A-VHI10). The A-AVI showed excellent test-retest reliability and internal consistency (intraclass correlation coefficient = 0.987 and Cronbach's alpha = 0.954, respectively). There was a significant difference in A-AVI scores between the elderly in the dysphonia and vocally healthy groups (P < 0.001). In addition, a significant correlation was demonstrated between A-AVI and A-VHI10 (r = 0.89). Unilateral vocal fold immobility and inflammatory laryngeal disorders were most frequently reported by the dysphonia group (28%). A-AVI has excellent validity and reliability in Arab-speaking elderly patients with voice disorders. It can be considered in the assessment of the effect of voice disorders on the quality of life of the elderly.

Arabic voice system to help illiterate or blind for using computer

Speech recognition is one type of technology, which make a computer to recognize the voice of words that an individual speaks through a microphone and convert it into the written text. In this paper, the proposed system for helping illiterate and blind peoples to open applications with them voices.The proposed system includes two parts; the first part is the training part while the second part is used for the testing. The system contains seven-steps; the first step is the recording of voices and the second step is voice pre-processing. The third step is the feature extraction using MFCC (Mel Frequency Cepstrum Coefficient) method that involves seven steps. The fourth step is for classification voices, there are 1400 voices samples used in training by using a Naïve Bayesian method as a classifier. The fifth step is the matching step using the Correlation Coefficient, there are 200 voices samples in testing. The sixth step was to convert voice into text and the seventh step for execution one of 20 commands.The accuracy of results from using the Naïve Bayesian algorithm in the training phase gives (100 %) while the accuracy of results in the testing phase using Correlation Coefficient gives (98%).

Al-Sharqawy’s Al-Ardd (The Land): An Ecocritical Exploration in Arabic Literature: A Cultural Approach

The whole village feared the new road that will swallow the land they had; the land that represents all of yesterday, today and the whole future. (Al-Ardd 86) It has been four years now since Nadine A. Sinno in her article, “The Greening of Modern Arabic Literature: An Ecological Interpretation of Two Contemporary Arabic Novels” called for a “project of incorporating Arab voices … in eco-critical studies” because, “the inclusive and embracing nature of the field (eco-criticism)—in addition to the rising significance of global environment concerns today—make it valid and even necessary for international scholars, including Arab scholars, to contribute to the growing eco-criticism discourse”(125). Similarly, Cheryll Glotfelty in The Ecocriticism Reader believed that “Eco-criticism has been predominantly a white movement. It will become a multi-ethnic movement when stronger connections are made between the environment and issues of social justice, and when a diversity of voices are to contribute to the discussion” (xxv). Ethnic authors and texts from non-western cultures are to share in this “global dialogue” and in fact, Arabic Literature is abundant in works which can be read through the lens of ecocriticism.

Arabic Discourse Analysis: A Naïve Algorithm for Defective Pronunciation Correction

This manuscript summarizes a novel study to detect and correct voice anomalies contained in Arabic discourses. These objectives are attained by following some fundamental steps. The first consists in classifying the Arabic produced healthy or pathological vocal signals. Second, the identification of problematic phonemes takes place. The proposition of algorithm allowing the correction of defective pronunciations presents the aim of the latter task. We are satisfied with the obtained results. In deed, the elaborated algorithm has attained a correction performance of 90% based on 52 Arabic voice sequences covering male and female, healthy and pathological speeches and clustering several areas. Consequently, researchers of topics related to speech processing can benefit from our proposition in the conception and development of their systems.

Theta Roles Assigned to Subject Argument in Arabic and English Passive Voice

This descriptive study sought to compare and contrast theta roles assigned to the passive voice arguments with special reference to the subject in Arabic and English. The findings of the study showed that both Arabic and English, though they belong to different language families, have close similarities concerning theta roles that could be assigned to the subject of the passive voice. The subject could have various theta roles (theme, patient, experiencer, source, goal, recipient, instrument, beneficiary, or location) in the two languages. The agent theta role could hardly be possible for a passive subject in the two languages. Agentive passive where the agent occurs in a prepositional phrase is more common in English than in Arabic. The study also showed that the recipient is preferably used as a subject of passive sentences with ditransitive verbs in the two languages. On the other hand, there are middle voice constructions in Arabic whose subject has a recipient theta role. The English counterpart for such constructions is passive voice. The study concluded that it is vitally important for Arab EFL learners to know these differences and similarities to help them understand the significance of maintaining the same theta roles of the arguments when they change sentences from active to passive or when they translate from Arabic into English or vice versa.

Voice Pathology Detection and Classification Using Auto-Correlation and Entropy Features in Different Frequency Regions

Automatic voice pathology detection and classification systems effectively contribute to the assessment of voice disorders, enabling the early detection of voice pathologies and the diagnosis of the type of pathology from which patients suffer. This paper concentrates on developing an accurate and robust feature extraction for detecting and classifying voice pathologies by investigating different frequency bands using autocorrelation and entropy. We extracted maximum peak values and their corresponding lag values from each frame of a voiced signal by using autocorrelation as features to detect and classify pathological samples. We also extracted the entropy for each frame of the voice signal after we normalized its values to be used as the features. These features were investigated in distinct frequency bands to assess the contribution of each band to the detection and classification processes. Various samples of the sustained vowel /a/ for both normal and pathological voices were extracted from three different databases in English, German, and Arabic. A support vector machine was used as a classifier. We also performed u-tests to investigate if there is a significant difference between the means of the normal and pathological samples. The best achieved accuracies in both detection and classification varied depending on the used band, method, and database. The most contributive bands in both detection and classification were between 1000 and 8000 Hz. The highest obtained accuracies in the case of detection were 99.69%, 92.79%, and 99.79% for Massachusetts eye and ear infirmary (MEEI), Saarbrucken voice database (SVD), and Arabic voice pathology database (AVPD), respectively. However, the highest achieved accuracies for classification were 99.54%, 99.53%, and 96.02% for MEEI, SVD, and AVPD, correspondingly, using the combined feature.

Correction of pathological speeches and assistance to learners with vocal disabilities

This work describes a new methodology for correcting voice defects contained in the Arabic speeches and assisting learners of Arabic vocabulary. For this purpose, we follow four stages. The first step consists in localizing the vocal disabilities which degrade an Arabic voice signal, so we focus on comparing between a referenced probabilistic-phonetic model and a speaker model. Second, we differentiate two cases: Degraded speeches can be generated from pathological problems, or it can be produced by non arabophone learners. Hence, we compare between forced alignment scores. Third, we develop a new algorithm to correct pathological pronunciations. The last task is the conception of an application assisting learners of Arabic vocabulary in improving their pronunciation. The achieved results are encouraging. Moreover, learners of Arabic vocabulary have presented a good amelioration using the developed application. A lot of applications that design systems of voice signal processing can use our proposition.

Plus ça Change: The 1922 U.S. Congressional Debate on the Balfour Declaration

This essay looks at the hearing held by the Foreign Affairs Committee of the U.S. House of Representatives in April 1922 on the subject of a Jewish National Home in Palestine, as well as the broader congressional debate over the Balfour Declaration at that crucial time. The landmark hearing, which took place against the backdrop of growing unrest in Palestine and just prior to the League of Nations' formal approval of Britain's Mandate over Palestine, offers a glimpse into the cultural and political mindset underpinning U.S. support for the Zionist project at the time as well as the ways in which the political discourse in the United States has, or has not, changed since then. Despite the overwhelming support for the Zionist project in Congress, which unanimously endorsed Balfour in September 1922, the hearing examined all aspects of the issue and included a remarkably diverse array of viewpoints, including both anti-Zionist Jewish and Palestinian Arab voices.

Arab Voices in Western Writing: The Politics of the Arabic Novel in English and the Anglophone Arab Novel

This article discusses the problems faced by Arab writers who wish to have their work taken up by Western publishing houses. It starts out from the assumption that, in the main, the latter demand reproduction of a specific repertoire of representations and images of Arabs. Arab authors – whether their writings are translated from Arabic or inscribed directly in western languages – have the option of acculturating to these requirements or run the risk of not getting into print. The article tests claims often made for Anglophone Arab novels that they are a form of postcolonial writing with both the potential and the achievement of circumventing translational bias and undermining stereotyping of Arabs and Islam.

From the Damascus Blood Libel to the “Arab Spring”: The Evolution of Arab Antisemitism

This article traces the development of antisemitism in the Arab world, outlining major clusters of the phenomenon—Islamic anti-Jewish motifs; classic Western antisemitic tropes; and major themes in Holocaust discourse. It also highlights Arab voices critical of Arab antisemitic discourse, bigotry, and incitement against Jews—a subject largely neglected in the scholarly literature thus far. As in other societies, Arab antisemitism is not a phenomenon that is isolated from other social and political trends, but is part and parcel of the ongoing debate between competing worldviews.

Development of the Arabic Voice Pathology Database and Its Evaluation by Using Speech Features and Machine Learning Algorithms.

A voice disorder database is an essential element in doing research on automatic voice disorder detection and classification. Ethnicity affects the voice characteristics of a person, and so it is necessary to develop a database by collecting the voice samples of the targeted ethnic group. This will enhance the chances of arriving at a global solution for the accurate and reliable diagnosis of voice disorders by understanding the characteristics of a local group. Motivated by such idea, an Arabic voice pathology database (AVPD) is designed and developed in this study by recording three vowels, running speech, and isolated words. For each recorded samples, the perceptual severity is also provided which is a unique aspect of the AVPD. During the development of the AVPD, the shortcomings of different voice disorder databases were identified so that they could be avoided in the AVPD. In addition, the AVPD is evaluated by using six different types of speech features and four types of machine learning algorithms. The results of detection and classification of voice disorders obtained with the sustained vowel and the running speech are also compared with the results of an English-language disorder database, the Massachusetts Eye and Ear Infirmary (MEEI) database.

Voice pathology detection using interlaced derivative pattern on glottal source excitation

Abstract In this paper, we propose a voice pathology detection and classification method using an interlaced derivative pattern (IDP), which involves an n -th order directional derivative, on a spectro-temporal description of a glottal source excitation signal. It is shown previously that directional information is useful to detect pathologies due to its encoding ability along time, frequency, and time-frequency axes. The IDP, being an n -th order derivative, is capable of describing more information than a first order derivative pattern by combining all the directional information into one. In the IDP, first-order derivatives are calculated in four directions, and these derivatives are thresholded with the center value of each directional channel to produce the final IDP. A support vector machine is used as a classification technique. Experiments are conducted using three different databases, which are the Massachusetts Eye and Ear Infirmary database, Saarbrucken Voice Database, and Arabic Voice Pathology Database. Experimental results show that the IDP based features give higher accuracy than that using other related features in all the three databases. The accuracies using cross-databases are also high using the IDP features.