Arabic Dialect Identification(DID)is a task in Natural Language Processing(NLP)that involves determining the dialect of a given piece of text in Arabic.The state-of-the-art solutions for DID are built on various deep ...Arabic Dialect Identification(DID)is a task in Natural Language Processing(NLP)that involves determining the dialect of a given piece of text in Arabic.The state-of-the-art solutions for DID are built on various deep neural networks that commonly learn the representation of sentences in response to a given dialect.Despite the effectiveness of these solutions,the performance heavily relies on the amount of labeled examples,which is labor-intensive to atain and may not be readily available in real-world scenarios.To alleviate the burden of labeling data,this paper introduces a novel solution that leverages unlabeled corpora to boost performance on the DID task.Specifically,we design an architecture that enables learning the shared information between labeled and unlabeled texts through a gradient reversal layer.The key idea is to penalize the model for learning source dataset specific features and thus enable it to capture common knowledge regardless of the label.Finally,we evaluate the proposed solution on benchmark datasets for DID.Our extensive experiments show that it performs signifcantly better,especially,with sparse labeled data.By comparing our approach with existing Pre-trained Language Models(PLMs),we achieve a new state-of-the-art performance in the DID field.The code will be available on GitHub upon the paper's acceptance.展开更多
This article makes the claim that the difference between typologically Bedouin and urban dialects of Arabic in gender representation in the plural, is a function of the urbanization process the urban dialects of Arabi...This article makes the claim that the difference between typologically Bedouin and urban dialects of Arabic in gender representation in the plural, is a function of the urbanization process the urban dialects of Arabic went through in the seventh century in the conquered territories. Contact induced linguistic processes of koineization and structural simplification in the newly established urban centers in the Middle East and North Africa immediately after the Arab conquests helped enhance the gender development that was already in effect before the Arab conquests. By comparing Bedouin and urban dialects to Classical Arabic, the article establishes that the three varieties were in a process of development in gender. Classical Arabic stopped at a particular stage, and Bedouin and urban dialects continued. Comparing Central Asian dialects to urban dialects of Egypt, they can see that at least to the eighth century, gender was a common feature of peninsular dialects. The article concludes by stating that the urban dialects developed further to lose all gender distinction in the plural because of the leveling and borrowing processes of the koineization in the urban centers in their formative period.展开更多
The Arabic Dialect (AD) detection method involves analyzing the matching sound wave for various characteristics that identify the speaker’s dialect. Among these features are accent, intonation, stress, vowel length, ...The Arabic Dialect (AD) detection method involves analyzing the matching sound wave for various characteristics that identify the speaker’s dialect. Among these features are accent, intonation, stress, vowel length, vowel type, and other acoustic characteristics. Data from different speakers of different dialects is usually used in training machine learning algorithms. Based on this data, an algorithm is created to accurately identify the speaker’s dialect. Arabic dialects can be detected and classified using several models and techniques available in literature. Various models have been proposed from different perspectives. Therefore, this paper discussed different studies about AD for building an understanding of conceptual deep learning model to detect and classify Arabic dialects. The model captured the semantic, syntactic, and phonological characteristics of these dialects using Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). The proposed model consists of six stages: Natural Language Processing (NLP) stage, feature engineering techniques, neural networks, language models, optimization techniques, and evaluation techniques. Each stage of the proposed model has several techniques that can be used to detect and classify AD. The accuracy and capability of the proposed model will be performed in the future work.展开更多
Arabic dialect identification is essential in Natural Language Processing(NLP)and forms a critical component of applications such as machine translation,sentiment analysis,and cross-language text generation.The diffic...Arabic dialect identification is essential in Natural Language Processing(NLP)and forms a critical component of applications such as machine translation,sentiment analysis,and cross-language text generation.The difficulties in differentiating between Arabic dialects have garnered more attention in the last 10 years,particularly in social media.These difficulties result from the overlapping vocabulary of the dialects,the fluidity of online language use,and the difficulties in telling apart dialects that are closely related.Managing dialects with limited resources and adjusting to the ever-changing linguistic trends on social media platforms present additional challenges.A strong dialect recognition technique is essential to improving communication technology and cross-cultural understanding in light of the increase in social media usage.To distinguish Arabic dialects on social media,this research suggests a hybrid Deep Learning(DL)approach.The Long Short-Term Memory(LSTM)and Bidirectional Long Short-Term Memory(BiLSTM)architectures make up the model.A new textual dataset that focuses on three main dialects,i.e.,Levantine,Saudi,and Egyptian,is also available.Approximately 11,000 user-generated comments from Twitter are included in this dataset,which has been painstakingly annotated to guarantee accuracy in dialect classification.Transformers,DL models,and basic machine learning classifiers are used to conduct several tests to evaluate the performance of the suggested model.Various methodologies,including TF-IDF,word embedding,and self-attention mechanisms,are used.The suggested model fares better than other models in terms of accuracy,obtaining a remarkable 96.54%,according to the trial results.This study advances the discipline by presenting a new dataset and putting forth a practical model for Arabic dialect identification.This model may prove crucial for future work in sociolinguistic studies and NLP.展开更多
Any natural language may have dozens of accents.Even though the equivalent phonemic formation of the word,if it is properly called in different accents,humans do have audio signals that are distinct from one another.A...Any natural language may have dozens of accents.Even though the equivalent phonemic formation of the word,if it is properly called in different accents,humans do have audio signals that are distinct from one another.Among the most common issues with speech,the processing is discrepancies in pronunciation,accent,and enunciation.This research study examines the issues of detecting,fixing,and summarising accent defects of average Arabic individuals in English-speaking speech.The article then discusses the key approaches and structure that will be utilized to address both accent flaws and pronunciation issues.The proposed SpeakCorrect computerized interface employs a cuttingedge speech recognition system and analyses pronunciation errors with a speech decoder.As a result,some of the most essential types of changes in pronunciation that are significant for speech recognition are performed,and accent defects defining such differences are presented.Consequently,the suggested technique increases the Speaker’s accuracy.SpeakCorrect uses 100 h of phonetically prepared individuals to construct a pronunciation instruction repository.These prerecorded sets are used to train Hidden Markov Models(HMM)as well as weighted graph systems.Their speeches are quite clear and might be considered natural.The proposed interface is optimized for use with an integrated phonetic pronounced dataset,as well as for analyzing and identifying speech faults in Saudi and Egyptian dialects.The proposed interface detects,analyses,and assists English learners in correcting utterance faults,overcoming problems,and improving their pronunciations.展开更多
基金supported by the Deanship of Scientific Research at King Khalid University through Small Groups funding(Project Grant No.RGP1/243/45)The funding was awarded to Dr.Mohammed Abker.And Natural Science Foundation of China under Grant 61901388.
文摘Arabic Dialect Identification(DID)is a task in Natural Language Processing(NLP)that involves determining the dialect of a given piece of text in Arabic.The state-of-the-art solutions for DID are built on various deep neural networks that commonly learn the representation of sentences in response to a given dialect.Despite the effectiveness of these solutions,the performance heavily relies on the amount of labeled examples,which is labor-intensive to atain and may not be readily available in real-world scenarios.To alleviate the burden of labeling data,this paper introduces a novel solution that leverages unlabeled corpora to boost performance on the DID task.Specifically,we design an architecture that enables learning the shared information between labeled and unlabeled texts through a gradient reversal layer.The key idea is to penalize the model for learning source dataset specific features and thus enable it to capture common knowledge regardless of the label.Finally,we evaluate the proposed solution on benchmark datasets for DID.Our extensive experiments show that it performs signifcantly better,especially,with sparse labeled data.By comparing our approach with existing Pre-trained Language Models(PLMs),we achieve a new state-of-the-art performance in the DID field.The code will be available on GitHub upon the paper's acceptance.
文摘This article makes the claim that the difference between typologically Bedouin and urban dialects of Arabic in gender representation in the plural, is a function of the urbanization process the urban dialects of Arabic went through in the seventh century in the conquered territories. Contact induced linguistic processes of koineization and structural simplification in the newly established urban centers in the Middle East and North Africa immediately after the Arab conquests helped enhance the gender development that was already in effect before the Arab conquests. By comparing Bedouin and urban dialects to Classical Arabic, the article establishes that the three varieties were in a process of development in gender. Classical Arabic stopped at a particular stage, and Bedouin and urban dialects continued. Comparing Central Asian dialects to urban dialects of Egypt, they can see that at least to the eighth century, gender was a common feature of peninsular dialects. The article concludes by stating that the urban dialects developed further to lose all gender distinction in the plural because of the leveling and borrowing processes of the koineization in the urban centers in their formative period.
文摘The Arabic Dialect (AD) detection method involves analyzing the matching sound wave for various characteristics that identify the speaker’s dialect. Among these features are accent, intonation, stress, vowel length, vowel type, and other acoustic characteristics. Data from different speakers of different dialects is usually used in training machine learning algorithms. Based on this data, an algorithm is created to accurately identify the speaker’s dialect. Arabic dialects can be detected and classified using several models and techniques available in literature. Various models have been proposed from different perspectives. Therefore, this paper discussed different studies about AD for building an understanding of conceptual deep learning model to detect and classify Arabic dialects. The model captured the semantic, syntactic, and phonological characteristics of these dialects using Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). The proposed model consists of six stages: Natural Language Processing (NLP) stage, feature engineering techniques, neural networks, language models, optimization techniques, and evaluation techniques. Each stage of the proposed model has several techniques that can be used to detect and classify AD. The accuracy and capability of the proposed model will be performed in the future work.
基金the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support(QU-APC-2024-9/1).
文摘Arabic dialect identification is essential in Natural Language Processing(NLP)and forms a critical component of applications such as machine translation,sentiment analysis,and cross-language text generation.The difficulties in differentiating between Arabic dialects have garnered more attention in the last 10 years,particularly in social media.These difficulties result from the overlapping vocabulary of the dialects,the fluidity of online language use,and the difficulties in telling apart dialects that are closely related.Managing dialects with limited resources and adjusting to the ever-changing linguistic trends on social media platforms present additional challenges.A strong dialect recognition technique is essential to improving communication technology and cross-cultural understanding in light of the increase in social media usage.To distinguish Arabic dialects on social media,this research suggests a hybrid Deep Learning(DL)approach.The Long Short-Term Memory(LSTM)and Bidirectional Long Short-Term Memory(BiLSTM)architectures make up the model.A new textual dataset that focuses on three main dialects,i.e.,Levantine,Saudi,and Egyptian,is also available.Approximately 11,000 user-generated comments from Twitter are included in this dataset,which has been painstakingly annotated to guarantee accuracy in dialect classification.Transformers,DL models,and basic machine learning classifiers are used to conduct several tests to evaluate the performance of the suggested model.Various methodologies,including TF-IDF,word embedding,and self-attention mechanisms,are used.The suggested model fares better than other models in terms of accuracy,obtaining a remarkable 96.54%,according to the trial results.This study advances the discipline by presenting a new dataset and putting forth a practical model for Arabic dialect identification.This model may prove crucial for future work in sociolinguistic studies and NLP.
基金funded by the National Plan for Science,Technology and Innovation(MAARIFAH)-King Abdulaziz City for Science and Technology(KACST)-Kingdom of Saudi Arabia-Project Number(10-INF-1406-03).
文摘Any natural language may have dozens of accents.Even though the equivalent phonemic formation of the word,if it is properly called in different accents,humans do have audio signals that are distinct from one another.Among the most common issues with speech,the processing is discrepancies in pronunciation,accent,and enunciation.This research study examines the issues of detecting,fixing,and summarising accent defects of average Arabic individuals in English-speaking speech.The article then discusses the key approaches and structure that will be utilized to address both accent flaws and pronunciation issues.The proposed SpeakCorrect computerized interface employs a cuttingedge speech recognition system and analyses pronunciation errors with a speech decoder.As a result,some of the most essential types of changes in pronunciation that are significant for speech recognition are performed,and accent defects defining such differences are presented.Consequently,the suggested technique increases the Speaker’s accuracy.SpeakCorrect uses 100 h of phonetically prepared individuals to construct a pronunciation instruction repository.These prerecorded sets are used to train Hidden Markov Models(HMM)as well as weighted graph systems.Their speeches are quite clear and might be considered natural.The proposed interface is optimized for use with an integrated phonetic pronounced dataset,as well as for analyzing and identifying speech faults in Saudi and Egyptian dialects.The proposed interface detects,analyses,and assists English learners in correcting utterance faults,overcoming problems,and improving their pronunciations.
