Arabic subtitles guide
Levantine Arabic Transcription: Why Your Subtitles Keep Getting It Wrong
Levantine Arabic is not one accent. Syrian, Lebanese, Jordanian, and Palestinian speakers share features but not every word, sound, or spelling. That variation is exactly what a generic “Arabic” subtitle setting hides.
One region, several everyday vocabularies
Even a word as basic as “now” can vary: هلا is common across Levantine contexts, while هسا is associated with Amman usage, هلقيت with Jerusalem usage, and إسّا with Galilee usage in the cited language references. Place words also vary: هون, هناك, هونيك, and هنيك signal local speech. A model that prefers one written form may erase that distinction.
The words that carry the sentence
| Spoken feature | Meaning or role | Why to review it |
|---|---|---|
| بدّي | I want | A high-frequency conversational form |
| شو | what | A question word unlike MSA ماذا |
| وين | where | A common spoken alternative to أين |
| هيك | like this | A short adverb that can be dropped |
| رح | future marker | A small particle that changes time |
| ب- prefix | present-tense marker | Attached grammar can disappear in word segmentation |
Published research shows the gap
A 2024 peer-reviewed study of Veed.io’s automatic speech recognition on Jordanian Arabic reported a 38.857% word error rate. Deletions made up most of the classified errors, followed by substitutions and insertions. The study is about one system and one Jordanian recording; it is not a universal score, but it is strong evidence that “Arabic supported” does not mean “Levantine transcript ready.”
Other research on zero-shot Whisper-style systems also reports much higher error on dialectal Arabic than on MSA, with meaningful differences among Syrian, Jordanian, Lebanese, and Palestinian test sets.
Our benchmark, labeled honestly
The table below is Kalemio’s own September 2026 evaluation on two hours of Levantine Arabic. It uses dialect-tolerant WER. It is not an independent study, and it does not predict Egyptian or Gulf performance.
| System | Dialect-tolerant WER |
|---|---|
| Kalemio | 4.6% |
| Meta Muse Voice 1.0 | 6.3% |
| Hamsa General V2 | 7.4% |
| ElevenLabs Scribe v2 | 7.4% |
| Groq Whisper large-v3 | 17.0% |
| Groq Whisper turbo | 25.1% |
What to check before publishing
- Play the original beside the subtitles and verify that dialect words were not “corrected” into MSA.
- Check short particles, prefixes, interjections, names, and code-switched words.
- Sample the beginning, middle, and end, especially where speakers overlap.
- Export the SRT and keep the reviewed file as your source of truth.
For the wider explanation, read why Arabic auto subtitles fail. To test your own recording, start with an Arabic SRT.
FAQ
Why is Levantine Arabic difficult for automatic subtitles?
It varies across Syrian, Lebanese, Jordanian, and Palestinian speech, has no single standard written form, and uses vocabulary and pronunciation that differ from formal Arabic.
Do Levantine dialects differ enough to affect accuracy?
Yes. Published research reports different error rates across Levantine varieties for the same model. Treat regional examples as review prompts, not as interchangeable labels.
How accurate is automatic Levantine transcription?
It varies by model, recording, and scoring method. The 2024 Jordanian study reported 38.857% WER for one commercial system; Kalemio’s own September 2026 evaluation measured 4.6% dialect-tolerant WER on two hours of Levantine Arabic.
What should I check in Levantine subtitles?
Look for بدّي, شو, وين, هيك, رح, and present-tense prefixes, as well as names and overlapping speech.
Sources and notes
Research checked 18 September 2026. Product prices and third-party features can change; verify them before making a purchasing decision.