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home / papers / 2026 / Ramsa: A Large Sociolinguistically Rich Emirati Arabic Speech Corpus for ASR and TTS

Ramsa: A Large Sociolinguistically Rich Emirati Arabic Speech Corpus for ASR and TTS

Summary

Speech resources for Gulf Arabic, and Emirati Arabic in particular, are small, gender-skewed, and usually treat the dialect as uniform. Ramsa addresses these gaps. It is a 41-hour corpus of spoken Emirati Arabic, still under development, built from two sources: structured interviews with university students and episodes of ten shows from government-run Emirati TV channels. The corpus records sociolinguistic metadata, including gender, age, birth emirate, and self-identified subdialect (Urban, Bedouin, Mountain/Shihhi, or Mixed). It also varies in interaction format, segment length, and acoustic conditions.

About 10% of the audio has been manually transcribed so far. On this subset, the author ran zero-shot benchmarks of commercial and open-source ASR and TTS systems. Whisper-large-v3-turbo produced the lowest ASR error rates, and MMS-TTS-Ara gave the most intelligible synthesis. All systems handled monologic and documentary material better than conversations with overlapping speech.

The resource gives Emirati Arabic a more representative testbed for speech technology and for studying dialect variation. Its remaining weaknesses are an Urban-heavy subdialect balance and transcription that is not yet complete.

Key points

  • Corpus composition:
    • 157 speakers (59 female, 98 male).
    • 91 monologic and 79 dialogic recordings.
    • Topics include heritage, agriculture and sustainability, cuisine, architecture, and careers.
  • Interview component:
    • 14 University of Sharjah students (13 women, 1 man), about 2 h 43 min in total.
    • 8 of the 14 participants described their subdialect as Mixed.
  • ASR baselines (zero-shot, average WER/CER):
    • Whisper-large-v3-turbo: 0.268 / 0.144
    • Gladia: 0.347 / 0.175
    • AssemblyAI: 0.354 / 0.175
  • TTS baselines (intelligibility, WER/CER):
    • MMS-TTS-Ara: 0.285 / 0.081
    • ArTST: 0.373 / 0.161
    • No MOS study was run, because expert listeners covering every subdialect were not available.
  • Access:
    • Interview audio and transcripts are available under a restricted Data Use Agreement for non-commercial research.
    • Broadcast media is not redistributed; only source URLs, metadata, and evaluation scripts are shared on request.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.63317/3fzxkpjoserh

@inproceedings{alsabbagh2026ramsa,
  title = {{Ramsa: A Large Sociolinguistically Rich Emirati Arabic Speech Corpus for ASR and TTS}},
  author = {Al-Sabbagh, Rania},
  booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference},
  year = {2026},
  doi = {10.63317/3fzxkpjoserh},
  url = {https://doi.org/10.63317/3fzxkpjoserh}
}

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