--- title: "Arabic Speech Recognition of zero-resourced Languages — Alrashoudi 2024" description: "The authors present this as evidence that transfer learning can bootstrap ASR for documenting endangered… 3.5% WER. 12 references, cited by 1 paper here." authors: ["Norah A. Alrashoudi", "Omar Said Alshahri", "Hend Al-Khalifa"] year: 2024 venue: "Proceedings of the 6th Workshop on Open-Source Arabic Corpora and Processing Tools (OSACT) with Shared Tasks on Arabic LLMs Hallucination and Dialect to MSA Machine Translation @ LREC-COLING 2024" source: "https://aclanthology.org/2024.osact-1.10/" licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)" licence_url: "https://creativecommons.org/licenses/by-nc/4.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/alrashoudi-2024-arabic-speech-recognition-zero-resourced/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/alrashoudi-2024-arabic-speech-recognition-zero-resourced/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2024](https://md.research.elamur.ai/free/en/papers/2024/page/5/) / Arabic Speech Recognition of zero-resourced Languages: A case of Shehri (Jibbali) Language # Arabic Speech Recognition of zero-resourced Languages: A case of Shehri (Jibbali) Language **Authors:** Norah A. Alrashoudi, Omar Said Alshahri and [Hend Al-Khalifa](https://md.research.elamur.ai/free/en/authors/hend-al-khalifa/) · **Year:** 2024 · **Venue:** Proceedings of the 6th Workshop on Open-Source Arabic Corpora and Processing Tools (OSACT) with Shared Tasks on Arabic LLMs Hallucination and Dialect to MSA Machine Translation @ LREC-COLING 2024 · **Licence:** [CC BY-NC 4.0 (ELRA, LREC proceedings)](https://creativecommons.org/licenses/by-nc/4.0/) **Links:** [Publisher's page](https://aclanthology.org/2024.osact-1.10/) · [PDF](https://aclanthology.org/2024.osact-1.10.pdf) · [OpenAlex](https://openalex.org/W7131793900) **Citations:** 1 citations (OpenAlex, 2026-10-14) · 1 in our library, of the 140,884 papers whose bibliographies we read ## Summary The paper tackles automatic speech recognition for Shehri (Jibbali), a Modern South Arabian language of Dhofar, Oman. The language has almost no annotated speech, no widely fixed writing system, and a shrinking number of young speakers. The authors built a small public speech corpus. Thirty speakers (23 male, 7 female) each read 15 sentences five times, giving about 1 h 54 min of audio. The sentences were chosen to contain Shehri sounds that Arabic lacks. Transcriptions use an Arabic-based alphabet with extra letters for those sounds, agreed after consulting the experts Watson and Al-Kathiri. Three pretrained speech model families were then fine-tuned on an 80/20 random split and scored with WER and CER: Wav2Vec2.0, HuBERT and Whisper. Whisper clearly won, and Whisper-medium reached 3.5% WER. The authors present this as evidence that transfer learning can bootstrap ASR for documenting endangered languages. One caveat follows from the setup: the corpus has only 15 distinct sentences, and utterances were shuffled before splitting. Test transcripts therefore also appear in training, so the scores likely overstate how well the models generalise. ## Key points - Corpus: 30 speakers × 15 sentences × 5 repetitions = 75 utterances per speaker, about 2,250 in total. - Speakers come from eastern and central Dhofar plus the adjacent western Asʼaib area. - Average clip length is about 3 s. - The corpus is released on GitHub (iwan-rg/Shehri-Jibbali-Speech-Dataset). - Split: random shuffle, then 80% train (about 1 h 31 min) and 20% test (about 22 min). - Models compared: Wav2Vec2.0, HuBERT and Whisper, evaluated with WER and CER. - Best result: Whisper-medium at 3.5% WER, well ahead of the self-supervised encoders. - Writing system: Shehri shares 25 consonant letters with Standard Arabic. It adds letters such as ڛ, ڸ and ڞ for sounds like /ɬ/, /ɮ/, /tʃʼ/ and ejectives, following Al-Kathiri et al. (2024). ## Interesting ideas - Build the elicitation material around exactly the phenomena the borrowed model has never seen (here, non-Arabic phonemes), so a tiny dataset spends its budget where transfer is weakest. - Fix the transcription convention with domain experts before annotating, so every label follows one agreed inventory instead of ad-hoc spellings. - Report two granularities of error (word-level and character-level) side by side, which separates near-miss spellings from completely wrong words. - Fine-tune several pretrained model families on the same small data and the same split, which turns the choice of base model into a measured decision rather than a default. - The design shows a trap: with few distinct sentences, shuffling individual recordings lets test items share text (and speakers) with training, which can inflate scores. ## For Mova How these ideas could serve Mova, our small open language model — its files, stages and tests: - **I1** (learn): when collecting teacher or silver data, target the constructions the witness parsers handle worst instead of sampling text uniformly. - **I2** (tree): before converting UD dialects or merging treebanks, freeze one explicit mapping of labels and features so the training signal stays consistent. - **I3** (tokens): score lemmas and morphology both as whole-token accuracy and at character or feature level, to tell small inflection slips from wrong analyses. - **I4** (learn): benchmark the perceptron parser and gparse variants on identical small silver sets before choosing which one to adapt. - **I5** (learn): split evaluation data by source text or document, and add a negative control on unseen sentences to expose this kind of leakage. *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](https://md.research.elamur.ai/free/en/shelves/linguistics/) · [Machine translation](https://md.research.elamur.ai/free/en/shelves/machine-translation/) Topics: [🎙️ Speech & spoken language](https://md.research.elamur.ai/free/en/topics/speech/) · [🐪 Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/) ## Cite Cite as: Norah A. Alrashoudi, Omar Said Alshahri and Hend Al-Khalifa. 2024. Arabic Speech Recognition of zero-resourced Languages: A case of Shehri (Jibbali) Language. Proceedings of the 6th Workshop on Open-Source Arabic Corpora and Processing Tools (OSACT) with Shared Tasks on Arabic LLMs Hallucination and Dialect to MSA Machine Translation @ LREC-COLING 2024. The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{alrashoudi2024arabic, title = {{Arabic Speech Recognition of zero-resourced Languages: A case of Shehri (Jibbali) Language}}, author = {Norah A. Alrashoudi, Omar Said Alshahri and Hend Al-Khalifa}, booktitle = {Proceedings of the 6th Workshop on Open-Source Arabic Corpora and Processing Tools (OSACT) with Shared Tasks on Arabic LLMs Hallucination and Dialect to MSA Machine Translation @ LREC-COLING 2024}, year = {2024}, doi = {10.63317/5ozhxig2mmtz}, url = {https://doi.org/10.63317/5ozhxig2mmtz} } ``` - [← previous Arabic Chatbot Technologies in Education: An Overview](https://md.research.elamur.ai/free/en/papers/bourhil-2025-arabic-chatbot-technologies-education-overview/) - [next → AraTar: A Corpus to Support the Fine-grained Detection of Hate Speech Targets in the Arabic Language](https://md.research.elamur.ai/free/en/papers/alghamdi-2024-aratar-corpus-support-fine-grained/) - [↑ back to papers of 2024](https://md.research.elamur.ai/free/en/papers/2024/page/5/) ## References 12 references, 4 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. Accessed: Nov. 06, 2023. [Online]. 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