---
title: "Building an automatic speech recognition system for Arabic"
description: "This paper, written in French, describes building an automatic speech recognition (ASR) system for Arabic using the open-source CMU…"
authors: ["Ali Sadiqui", "Noureddine Chenfour"]
year: 2010
venue: "arXiv:1202.6266 · Ann. Univ. Tibiscus Comp. Sci. Series VIII / 1 (2010), 27-40"
source: "https://arxiv.org/abs/1202.6266"
doi: "10.48550/arxiv.1202.6266"
arxiv: "1202.6266"
licence: "arXiv — non-exclusive license to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
title_orig: "Realisation d'un systeme de reconnaissance automatique de la parole arabe base sur CMU Sphinx"
title_lang: "fr"
canonical: "https://research.elamur.ai/free/en/papers/sadiqui-2012-realisation-d-un-systeme-de/"
---
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# Building an automatic speech recognition system for Arabic based on CMU Sphinx

*Original (French): Realisation d'un systeme de reconnaissance automatique de la parole arabe base sur CMU Sphinx*

**Authors:** Ali Sadiqui and Noureddine Chenfour · **Year:** 2010 · **Venue:** arXiv:1202.6266 · Ann. Univ. Tibiscus Comp. Sci. Series VIII / 1 (2010), 27-40 · **Licence:** [arXiv — non-exclusive license to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/1202.6266) · [PDF](https://arxiv.org/pdf/1202.6266) · [DOI 10.48550/arxiv.1202.6266](https://doi.org/10.48550/arxiv.1202.6266) · [arXiv 1202.6266](https://arxiv.org/abs/1202.6266) · [OpenAlex](https://openalex.org/W7094373525)

**Citations:** 2 citations (OpenAlex, 2026-10-14)

## Summary
This paper, written in French, describes building an automatic speech recognition (ASR) system for Arabic using the open-source CMU Sphinx toolkit. The authors address the limited ASR tooling for Arabic by adapting an existing, general-purpose recognizer instead of building one from scratch. The work appears to cover the standard Sphinx pipeline: preparing Arabic speech data, training models, decoding, and reporting recognition results. Page 11 contains a large results table. The extracted PDF text is heavily font-corrupted, so the specific corpus, model configuration and numeric scores cannot be reliably recovered and are not reported here. The paper's main value is as a case study of porting a mature, language-independent ASR framework to an under-served language. That is a practical route for any team bootstrapping speech technology for a new language.

## Key points
- Authors: Ali Sadiqui and Noureddine Chenfour. It appeared in the Annals of the University of Tibiscus, Computer Science Series, volume VIII, issue 1 (2010), on pages 27–40, and is also on arXiv as 1202.6266.
- The system is built on CMU Sphinx, an open-source, statistically trained speech recognition framework.
- The target language is Arabic, which the authors treat as under-resourced for ASR.
- Results are given as a per-item table (p. 11). The figures are unreadable in the extracted text, so no accuracy numbers are cited here.
- The paper is written in French, which places it in the North-African francophone research context.

## Interesting ideas
- Port an existing language-independent toolkit to a new language and invest the effort in language-specific resources, not in a new engine
- Report results per item in a detailed table, not only as one aggregate score, so that systematically hard cases stand out
- Treat a mature open framework's documented pipeline (data preparation, training, decoding) as a reproducible recipe for a low-resource setting

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (tokens): the same strategy could extend crate en's tokenizer and morphology to new varieties by swapping lexicons and rules while keeping the core machinery untouched.
- **I2** (learn): evaluation runs could publish per-construction or per-label breakdowns, which makes regressions and weak spots explainable rather than hidden in an average.
- **I3** (tree): a fixed, scripted recipe for training gparse or the perceptron parser on a new treebank would make UD dialect experiments repeatable and comparable.

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

📚 Shelves (5): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 9,086 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 8,889 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 9,296 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 9,206 works · [🔊 Signal Processing](https://md.research.elamur.ai/free/en/shelves/computer-science/signal-processing/) · 213 works

🧭 Topics (2): [Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/) · 1,484 works · [🎙️ Speech & spoken language](https://md.research.elamur.ai/free/en/topics/speech/) · 233 works

## Cite

Cite as: Ali Sadiqui and Noureddine Chenfour. 2010. Realisation d'un systeme de reconnaissance automatique de la parole arabe base sur CMU Sphinx. arXiv:1202.6266 · Ann. Univ. Tibiscus Comp. Sci. Series VIII / 1 (2010), 27-40.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{sadiqui2010realisation,
  title = {{Realisation d'un systeme de reconnaissance automatique de la parole arabe base sur CMU Sphinx}},
  author = {Ali Sadiqui and Noureddine Chenfour},
  year = {2010},
  doi = {10.48550/arxiv.1202.6266},
  eprint = {1202.6266},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.1202.6266}
}
```

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*Updated 24 October 2026*