Introducing Falcon ASR

English · العربية

Arabic WER: 20.92% · Parameters: 1.6B · Emirati WER (TII evaluation): 22.73%

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.

Try Falcon ASR →

Recognising spoken Arabic

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.

We trained Falcon-ASR on Emirati, MSA, other Gulf and Arabic dialects, and English. Our aim is to transcribe the words people use in everyday speech, including dialectal forms and changes between languages.

Arabic benchmark results

The Open Universal Arabic ASR Leaderboard, maintained by the ELM Research Center, ranks systems by the equal-weight average WER across six test sets. It also reports character error rate (CER). Lower values are better for both metrics. Our Falcon-ASR evaluation follows this protocol.

WER = Word Error Rate; CER = Character Error Rate. A lower value indicates better performance.

Evaluating Emirati speech

In our internal Emirati evaluation, Falcon-ASR achieved 22.73% WER and 10.19% CER: