Blog · Explainer

Levantine Arabic transcription: why it is hard and how to fix it

By the Echo team · Published 25 October 2026 · 6 min read

Levantine Arabic is the everyday spoken dialect of Syria, Lebanon, Jordan and Palestine. You hear it in offices, on phone calls and in meetings across the Gulf, often in the same conversation as English. The dialect matters for a transcript because it is not the language most people write. Modern Standard Arabic, the formal language of writing and news, differs from the spoken dialects in sound, vocabulary and grammar. The glossary defines Levantine Arabic and the other dialects.

What makes Levantine speech hard for transcription tools?

Three problems explain most weak Levantine transcripts.

1. There is less labelled speech to learn from. A speech tool learns from recordings that people have listened to and typed out. A 2018 University of Edinburgh PhD thesis by Ahmed Ali names this gap as the first of three challenges for dialectal Arabic. The other two are training robust models from limited labelled data, and evaluating dialects that have no spelling rules.
2. There is no fixed spelling. People write Levantine informally, so one spoken word can appear in several written forms. A tool may choose a spelling your team does not use, and a correct word can look wrong in your notes.
3. Speakers switch languages mid-sentence. A typical line sounds like «بدنا نعمل follow-up مع الـclient», which means “we need to do a follow-up with the client.” The English words sit inside an Arabic sentence, so the tool has to handle both languages in the same line.

The vocabulary also changes. These common Levantine words have formal Arabic equivalents that a reader of Modern Standard Arabic would expect:

Your notes will read more naturally, and search will find more, if your team decides how these words are written before the first recording.

What does the research say about Levantine speech recognition?

Four published papers, all free to read online, show the same pattern.

In 2005, Dimitra Vergyri, Katrin Kirchhoff and their colleagues described a large-vocabulary recognizer for Levantine telephone conversations, built by adapting an existing system to a new dialect. They compared grapheme-based and phoneme-based acoustic models, automatic vowelization and morphological language models, and reported state-of-the-art results on the 2004 Levantine evaluation. The abstract gives no error rates, so it shows what was tried, not how well a product works today. The paper is in the Interspeech 2005 archive.

In 2011, Sarah Al-Shareef and Thomas Hain used Levantine telephone conversations as a case study of how colloquial Arabic differs from Modern Standard Arabic. They built separate data sets for the dialects, measured how much they overlapped with formal Arabic sources, and concluded that better information is needed to choose acoustic models. Read the 2011 paper.

The 2018 Edinburgh thesis reaches the same conclusion from the data side. It names the shortage of labelled dialect speech and the absence of standard spelling as the core problems, and it treats Levantine as one of the main dialectal varieties, alongside Egyptian and Gulf Arabic.

In 2024, a benchmark for Arabic speech recognition on telephone calls, by Qusai Abo Obaidah and colleagues, listed the difficulties of real calls: dialect diversity, phonetic complexity, poor audio quality, background noise and conversational style. Their arXiv paper is the most recent of the four.

None of these papers measures a tool you can buy today. What they agree on is that dialect speech has less training data, no fixed spelling, and suffers more from noise and poor recordings. So judge any tool on a few minutes of your own Levantine recordings, not on a brochure.

How do you get a clean Levantine transcript?

You cannot remove the problems above, but you can reduce each one.

1. Record close to the speakers. Put the phone in the middle of the table, nearer the quieter voices, and turn off fans, music and notifications. Background noise and voices talking over each other make any tool worse, and the 2024 benchmark names both as hard conditions.
2. Test the tool on your own speech first. Record five minutes of a real meeting, with your team's accents and names, and check the transcript line by line against the audio. A demo with one clear speaker tells you little.
3. Decide how dialect words are written. Choose one form for each common word, for example شو or ماذا, and write that choice into your notes template. Consistent spelling makes search work later.
4. List names and product terms before the meeting. Names of people, clients and products are the first thing a transcript gets wrong. Put them in the agenda with the spelling you want. The meeting minutes template has a place for them.
5. Check decisions, numbers and owners against the audio. A summary is a shortcut. The transcript is the record you check it against.

For the wider picture, read Arabic dialects and code-switching. For the other dialects, read Gulf Arabic transcription and Egyptian Arabic transcription.

Can Echo transcribe Levantine Arabic mixed with English?

Echo's Arabic support covers Levantine, Gulf, Egyptian, Maghrebi and Modern Standard Arabic, including recordings that mix Arabic with English. On iPhone, Echo records the room through the phone's microphone. On Mac, it records from the menu bar using the microphone and the computer's audio, so no bot or extra participant joins a Zoom, Google Meet or Microsoft Teams call.

Each recording comes back with a full transcript with timestamps, a summary of the key points and decisions, and a to-do list. Ask Echo can answer questions from one note or across all your notes. No tool gets every word right, so use the review steps above before you rely on a note.

Do you need permission to record a Levantine conversation?

You should tell people and ask before you record, every time. The law differs from country to country, so check the official guidance from your country's data protection or telecommunications authority before you record a call or a room. This article is not legal advice.

Where Echo fits

Echo is an AI notetaker for iPhone and Mac. It turns Levantine, Gulf, Egyptian and mixed Arabic-English meetings into searchable notes. The free plan includes 30 minutes of recording a month, up to 15 minutes per recording, with a transcript, summary and to-dos. Echo Pro removes the monthly minute limit and allows recordings of up to four hours. In the UAE App Store it costs AED 149.99 a year with a 3-day free trial, or AED 12.99 a week. Prices for your region appear at checkout; see pricing.

Frequently asked questions

Sources

Checked on 25 October 2026.

• Development of a conversational telephone speech recognizer for Levantine Arabic, Interspeech 2005, ISCA Archive
• An investigation in speech recognition for colloquial Arabic, Interspeech 2011, ISCA Archive
• Multi-dialect Arabic broadcast speech recognition, PhD thesis, Ahmed Ali, University of Edinburgh (2018)
• A New Benchmark for Evaluating Automatic Speech Recognition in the Arabic Call Domain, arXiv (2024)

Keep reading