Diacritic Recognition Performance in Arabic ASR

In Arabic text, most vowels are encoded in the form of diacritics that are often omitted, so most speech corpora and ASR models are undiacritized. Text-based diacritization has previously been used to preprocess the input or post-processs ASR hypotheses. It is generally believed that input diacritization degrades ASR quality, but no systematic evaluation of ASR diacritization performance has been conducted to date. We experimentally clarify whether input diacritiztation indeed degrades ASR quality and compare ASR diacritization with text-based diacritization. We fine-tune pre-trained ASR models on transcribed speech with different diacritization conditions: manual, automatic, and no diacritization. We isolate diacritic recognition performance from the overall ASR performance using coverage and precision metrics. We find that ASR diacritization significantly outperforms text-based diacritization, particularly when the ASR model is fine-tuned with manually diacritized transcripts.