| Challenge: | acoustic modeling, large text data and a pronunciation lexicon are the bottlenecks for speech recognition systems for resource scarce languages. |
| Approach: | They propose a grapheme-to-phoneme conversion tool that generates a pronunciation lexicon from a list of Urdu words. |
| Outcome: | The proposed tool predicts pronunciation of words using a LSTM-based model trained on a handcrafted expert lexicon of around 39,000 words and shows an accuracy of 64% upon internal evaluation. |
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Jackson L. Lee, Lucas F.E. Ashby, M. Elizabeth Garza, Yeonju Lee-Sikka, Sean Miller, Alan Wong, Arya D. McCarthy, Kyle Gorman
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| Challenge: | Existing treebanks for Urdu are under-resourced due to lack of resources. |
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| Challenge: | Recent advances in distributional semantics have led to the rise of neural network-based models that use unsupervised learning to represent words as dense, distributed vectors, called 'word embeddings' embedders hold key to improving natural language processing for low-resource languages, since they require significant time and manpower. |
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Sarfraz Ahmad, Hasan Iqbal, Momina Ahsan, Numaan Naeem, Muhammad Ahsan Riaz Khan, Arham Riaz, Muhammad Arslan Manzoor, Yuxia Wang, Preslav Nakov
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