The Arabic language comes under the category of Semitic languages with an entirely different sentence structure in terms of Natural Language Processing. In such languages, two different words may have identical spelli...The Arabic language comes under the category of Semitic languages with an entirely different sentence structure in terms of Natural Language Processing. In such languages, two different words may have identical spelling whereas their pronunciations and meanings are totally different. To remove this ambiguity, special marks are put above or below? the spelling characters to determine the correct pronunciation. These marks are called diacritics and the language that uses them is called a diacritized language. This paper presents a system for Arabic language diacritization using Hid- den Markov Models (HMMs). The system employs the renowned HMM Tool Kit? (HTK). Each single diacritic is represented as a separate model. The concatenation of output models is coupled with the input? character sequence to form the fully diacritized text. The performance of the proposed system is assessed using a data corpus that includes more than 24000 sentences.展开更多
The attention-based encoder-decoder technique,known as the trans-former,is used to enhance the performance of end-to-end automatic speech recognition(ASR).This research focuses on applying ASR end-toend transformer-ba...The attention-based encoder-decoder technique,known as the trans-former,is used to enhance the performance of end-to-end automatic speech recognition(ASR).This research focuses on applying ASR end-toend transformer-based models for the Arabic language,as the researchers’community pays little attention to it.The Muslims Holy Qur’an book is written using Arabic diacritized text.In this paper,an end-to-end transformer model to building a robust Qur’an vs.recognition is proposed.The acoustic model was built using the transformer-based model as deep learning by the PyTorch framework.A multi-head attention mechanism is utilized to represent the encoder and decoder in the acoustic model.AMel filter bank is used for feature extraction.To build a language model(LM),the Recurrent Neural Network(RNN)and Long short-term memory(LSTM)were used to train an n-gram word-based LM.As a part of this research,a new dataset of Qur’an verses and their associated transcripts were collected and processed for training and evaluating the proposed model,consisting of 10 h of.wav recitations performed by 60 reciters.The experimental results showed that the proposed end-to-end transformer-based model achieved a significant low character error rate(CER)of 1.98%and a word error rate(WER)of 6.16%.We have achieved state-of-the-art end-to-end transformer-based recognition for Qur’an reciters.展开更多
Can Arabic be an unconventional method for teaching English？ This research will describe how the teacher used some Arabic language methods as a teaching strategy to improve her EFL students＇ reading, writing, and un...Can Arabic be an unconventional method for teaching English？ This research will describe how the teacher used some Arabic language methods as a teaching strategy to improve her EFL students＇ reading, writing, and understanding of English grammar. The research took place over a period of two years in Lebanon and four years in Saudi Arabia. Data consists of comparative tables, videos of two samples of students using Arabic to learn English, and pictures of the teacher using Arabic for comparative in grammar. Results revealed an increase in the level of understanding and comprehension of students in both the elementary and intermediate levels.展开更多
End-to-end scene text spotting,which jointly localizes and recognizes texts in natural images,has advanced significantly for Chinese and English.However,Vietnamese text spotting remains challenging due to persistent d...End-to-end scene text spotting,which jointly localizes and recognizes texts in natural images,has advanced significantly for Chinese and English.However,Vietnamese text spotting remains challenging due to persistent diacritic recognition failures and missed detections.To bridge this gap,we proposed a diacritic-focused Vietnamese text spotting framework that mitigates background interference.Specifically,we proposed the DDCM to capture fine-grained diacritical features by adapting to the structural characteristics of Vietnamese character.During the detection phase,we proposed the Global Feature Fusion Module to help the model more accurately understand the relationship between local details and global context for each region of interest.During the recognition phase,we designed the Cross Channel Attention Module to capture the spatial relationships while discriminating subtle diacritic variations through channel-wise recalibration.Extensive experiments demonstrate that our framework improves recognition accuracy over several state-of-the-art methods on Vietnamese scene text benchmarks.The code is available at https://github.com/mlmmwym/FCVintextSpotter.展开更多
文摘The Arabic language comes under the category of Semitic languages with an entirely different sentence structure in terms of Natural Language Processing. In such languages, two different words may have identical spelling whereas their pronunciations and meanings are totally different. To remove this ambiguity, special marks are put above or below? the spelling characters to determine the correct pronunciation. These marks are called diacritics and the language that uses them is called a diacritized language. This paper presents a system for Arabic language diacritization using Hid- den Markov Models (HMMs). The system employs the renowned HMM Tool Kit? (HTK). Each single diacritic is represented as a separate model. The concatenation of output models is coupled with the input? character sequence to form the fully diacritized text. The performance of the proposed system is assessed using a data corpus that includes more than 24000 sentences.
基金the Chair of Prince Faisal for Artificial Intelligent research(CPFIA),Qassim University through the Project Number QU-CPFAI-2-10-5.
文摘The attention-based encoder-decoder technique,known as the trans-former,is used to enhance the performance of end-to-end automatic speech recognition(ASR).This research focuses on applying ASR end-toend transformer-based models for the Arabic language,as the researchers’community pays little attention to it.The Muslims Holy Qur’an book is written using Arabic diacritized text.In this paper,an end-to-end transformer model to building a robust Qur’an vs.recognition is proposed.The acoustic model was built using the transformer-based model as deep learning by the PyTorch framework.A multi-head attention mechanism is utilized to represent the encoder and decoder in the acoustic model.AMel filter bank is used for feature extraction.To build a language model(LM),the Recurrent Neural Network(RNN)and Long short-term memory(LSTM)were used to train an n-gram word-based LM.As a part of this research,a new dataset of Qur’an verses and their associated transcripts were collected and processed for training and evaluating the proposed model,consisting of 10 h of.wav recitations performed by 60 reciters.The experimental results showed that the proposed end-to-end transformer-based model achieved a significant low character error rate(CER)of 1.98%and a word error rate(WER)of 6.16%.We have achieved state-of-the-art end-to-end transformer-based recognition for Qur’an reciters.
文摘Can Arabic be an unconventional method for teaching English？ This research will describe how the teacher used some Arabic language methods as a teaching strategy to improve her EFL students＇ reading, writing, and understanding of English grammar. The research took place over a period of two years in Lebanon and four years in Saudi Arabia. Data consists of comparative tables, videos of two samples of students using Arabic to learn English, and pictures of the teacher using Arabic for comparative in grammar. Results revealed an increase in the level of understanding and comprehension of students in both the elementary and intermediate levels.
基金partially supported by the National Natural Science Foundation of China(62366011)the Natural Science Foundation of Guangxi District(2024GXNSFDA010066)the Key R&D Program of Guangxi under Grant(AB21220023)。
文摘End-to-end scene text spotting,which jointly localizes and recognizes texts in natural images,has advanced significantly for Chinese and English.However,Vietnamese text spotting remains challenging due to persistent diacritic recognition failures and missed detections.To bridge this gap,we proposed a diacritic-focused Vietnamese text spotting framework that mitigates background interference.Specifically,we proposed the DDCM to capture fine-grained diacritical features by adapting to the structural characteristics of Vietnamese character.During the detection phase,we proposed the Global Feature Fusion Module to help the model more accurately understand the relationship between local details and global context for each region of interest.During the recognition phase,we designed the Cross Channel Attention Module to capture the spatial relationships while discriminating subtle diacritic variations through channel-wise recalibration.Extensive experiments demonstrate that our framework improves recognition accuracy over several state-of-the-art methods on Vietnamese scene text benchmarks.The code is available at https://github.com/mlmmwym/FCVintextSpotter.
