Automatic Speech Recognition Diacritization

Automatic speech recognition (ASR) diacritization focuses on automatically adding diacritical marks—symbols indicating pronunciation—to text generated by ASR systems, particularly for languages like Arabic and Hebrew where these marks are crucial for accurate interpretation. Current research emphasizes leveraging pre-trained language models, such as transformers (e.g., BERT variants) and LSTMs, often in a transfer learning framework, to improve diacritization accuracy and efficiency. This research is significant because accurate diacritization enhances the usability of ASR outputs for downstream NLP tasks and improves the overall quality of text processing for languages relying heavily on diacritics, impacting fields like machine translation and language learning.