I am interested in publishing research on deep learning-based automatic speech recognition (ASR) for a specific low-resource language. What are the key areas or research gaps that I should focus on to contribute to this field? Are there any influential papers or research works that I should read to gain a comprehensive understanding of the current state-of-the-art in this area? I would greatly appreciate any guidance or recommendations from experts in this field. Thank you in advance!
The research gaps include data scarcity, lack of text transcriptions, challenges with out-of-vocabulary words, and handling accent and dialect variability. Additionally, code-switching and multilingualism, model adaptation, and robustness to adverse acoustic conditions are important areas for exploration.
You can focus on developing novel data augmentation techniques, effective transfer learning and multilingual modeling, and exploring unsupervised and semi-supervised learning methods. Investigate robustness to adverse acoustic conditions, devise language-specific modeling approaches, and work on active learning and data collection strategies to optimize the annotation process.
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