We’re introducing Falcon-ASR, our 1.6 billion parameter speech recognition model for Arabic, with a particular focus on the Emirati dialect. Developed at the Technology Innovation Institute (TII) in Abu Dhabi, it also supports English, French, Spanish and Portuguese.
In our evaluation, Falcon-ASR achieved an average word error rate of 20.92% across six Arabic test sets, compared with the best published result of 23.17% in the leaderboard snapshot we used. On our internal Emirati evaluation, it recorded the lowest word and character error rates among the systems we compared.
We also support word-level timestamps for transcriptions, linking each transcribed word to its position in the audio.
Arabic speech varies by region, speaker and setting. A model that handles a formal news broadcast may still struggle with a conversation in Emirati or with speech recorded over a phone line. Dialectal Arabic also has fewer transcribed resources than Modern Standard Arabic (MSA), which makes training and evaluation harder.
