Monitoring AI enhancements in Urdu
Latest Urdu-focused AI updates and research-backed news
4 days agoFreeUrduAIWorkshop 2026 was organised at The IslamiaUniversityof Bahawalpur (IUB), focusing on the balanced integration of social sciences and artificial intelligence. The academic session brought together faculty members and senior scholars who discussed the growing role ofAIin education and ..
Open Full NewsAug 22, 2024Machinetranslationhas revolutionized the field of languagetranslationin the last decade. Initially dominated by statistical models, the rise of deep learning techniques has led to neural networks, particularly Transformer models, taking the lead. These models have demonstrated exceptional performance in natural language processing tasks, surpassing traditional sequence-to-sequence models ...
Open Full NewsAbstract Recent advancements in NeuralMachineTranslation(NMT) systems have significantly improved model performance on varioustranslationbenchmarks. However, these systems still face numerous challenges when translating low-resource languages such asUrdu. In this work, we highlight the specific issues faced bymachinetranslationsystems when translatingUrdulanguage. We first conduct a ...
Open Full NewsMachinetranslationsystems have witnessed significant advancements in various tasks, raising questions about their performance for low-resource languages, particularly those based on Indo-Aryan scripts likeUrdu. This study delves into the challenges faced bymachinetranslationsystems when dealing withUrdu, a low-resource Indo-Aryan language.
Open Full News5 days agoAbstract Recent advancements in NeuralMachineTranslation(NMT) systems have significantly improved model performance on varioustranslationbenchmarks. However, these systems still face numerous challenges when translating low-resource languages such asUrdu.
Open Full NewsDec 9, 2024This blog post delves into a unique project combiningdeeplearningand natural language processing (NLP) to create a pipeline forUrduspeech-to-text transcription, back translation, fine-tuning ...
Open Full NewsSpeechrecognition, a disruptive technology, has revolutionized human-machine interaction. While numerous AutomaticSpeechRecognition(ASR) models are publicly available via HuggingFace, the majority cater to English language. ForUrdu, however, models are scarce or closed-source, with open-sourced ones often lack the robustness. Our research addresses this scarcity, focusing on the ...
Open Full NewsSep 19, 2025SpeechEmotionRecognition(SER) aims to identify human emotions from vocal expressions, contributing significantly to affective computing applications. Despite progress in SER for widely spoken languages,Urduremains underrepresented due to the lack of dedicated datasets and language-specific modeling. This study proposes adeeplearningframework specifically designed forUrduSER ...
Open Full NewsAutomaticSpeechRecognition, (ASR) has achieved the best results for English, with end-to-end neural network based supervised models. These supervised models need huge amounts of labeledspeechdata for good generalization, which can be quite a challenge to obtain for low-resource languages likeUrdu. Most models proposed forUrduASR are based on Hidden Markov Models (HMMs). This paper ...
Open Full NewsMay 17, 2024Moreover, the large number of unique symbols inUrdu(Fig 1(a)), including diacritics (Fig 1(b)), makes handwrittenUrdutext recognition particularly challeng-ing [5, 6]Urduis a cursivelanguagewith 12 different writing styles, making text recognition chal-lenging.
Open Full NewsAug 1, 2024For this purpose, this study considers and proposes two word predictionmodelsforUrdu. Firstly, we propose to use LSTM for neurallanguagemodeling ofUrdu. LSTMs are a popular approach forlanguagemodeling due to their ability to process sequential data. Secondly, we employ BERT which was specifically designed for naturallanguagemodeling.
Open Full NewsJul 5, 2024Thisresearchunderscores the complex interplay between data,modelarchitecture, and evaluation metrics asAIevolves to accommodate the world's diverselanguages. The future ofUrduNLP may well lie in harnessing the strengths of both generalist and specialistAIs.
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