---
title: "An ensemble-based framework for — Calik 2023"
description: "The paper addresses automatic detection of mispronounced Arabic phonemes, aimed at computer-assisted language learning (CALL) and especially at Quranic…"
authors: ["Sukru Selim Calik", "Ayhan Kucukmanisa", "Zeynep Hilal Kilimci"]
year: 2023
venue: "arXiv:2301.01378"
source: "https://arxiv.org/abs/2301.01378"
doi: "10.48550/arxiv.2301.01378"
arxiv: "2301.01378"
licence: "CC BY-NC-ND 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by-nc-nd/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/calik-2023-ensemble-based-framework-mispronunciation-detection/"
---
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# An ensemble-based framework for mispronunciation detection of Arabic phonemes

**Authors:** [Sukru Selim Calik](https://md.research.elamur.ai/free/en/authors/sukru-selim-calik/), [Ayhan Kucukmanisa](https://md.research.elamur.ai/free/en/authors/ayhan-kucukmanisa/), [Zeynep Hilal Kilimci](https://md.research.elamur.ai/free/en/authors/zeynep-hilal-kilimci/) · **Year:** 2023 · **Venue:** arXiv:2301.01378 · **Licence:** [CC BY-NC-ND 4.0 (arXiv)](http://creativecommons.org/licenses/by-nc-nd/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2301.01378) · [PDF](https://arxiv.org/pdf/2301.01378) · [DOI 10.48550/arxiv.2301.01378](https://doi.org/10.48550/arxiv.2301.01378) · [arXiv 2301.01378](https://arxiv.org/abs/2301.01378) · [OpenAlex](https://openalex.org/W4313598221)

**Citations:** 0 citations (openalex-snapshot · 2026-10-14) · [3](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
The paper addresses automatic detection of mispronounced Arabic phonemes, aimed at computer-assisted language learning (CALL) and especially at Quranic (Classical Arabic) recitation, where pronunciation rules are strict. The authors recorded their own dataset: 29 Arabic letters voiced by 11 speakers, 8 of them hafiz. They enlarged it with four audio augmentations: added noise, time shifting, time stretching and pitch shifting. Two acoustic representations, MFCC and Mel spectrogram, were each fed to classical classifiers (SVM, k-NN, decision tree, naive Bayes, random forest). These were then combined through voting, bagging, boosting and three stacking variants. Mel spectrogram features clearly beat MFCC across the board. A voting ensemble on Mel spectrograms reached 95.9% accuracy, which outperformed two reimplemented deep sound-classification networks on the same data and also ran fastest. The work matters because it shows that cheap, low-complexity models can match or beat deep learning on a small speech task. That makes them suitable for embedded or low-resource deployment.

## Key points
- Own dataset: 29 Arabic letters, 11 speakers (8 hafiz), augmented with noise, time shift, time stretch and pitch shift. The data uses an 80/20 train/test split plus 5-fold cross-validation.
- With MFCC input, the best single model was SVM (accuracy 0.758), and the best ensembles stayed below about 0.78. With Mel spectrogram input, k-NN (k=3) reached 0.953 and SVM reached 0.952.
- The best system was a voting ensemble on Mel spectrograms, with accuracy 0.959, precision 0.969, recall 0.957 and F1 0.962. Stacking with random forest followed at 0.951.
- Two deep baselines were reimplemented on the same dataset: Abdoli et al. 2019 reached 0.910 accuracy and Choi et al. 2018 reached 0.900. Their processing times were 0.470 s and 0.612 s, compared with 0.091 s for the proposed method.
- The text reports the headline figure inconsistently: 95.9% in the abstract and tables, but 95.3% in the introduction.

## Interesting ideas
- The choice of input representation shifted accuracy by roughly 20 points, far more than the choice of classifier, so features deserve tuning before the learner does. — For Mova (tokens): Before swapping tagger algorithms, compare alternative feature templates for the en tagger and morphology on the same split to find where the real gain lies.
- A simple majority vote over several cheap, diverse classifiers matched or beat heavier stacking and deep models while staying fast. — For Mova (learn): A plain vote over the witness parsers is a strong, explainable baseline for building silver trees, and any fancier aggregation should have to beat it.
- Baselines were reimplemented from their original specifications and run on the very same dataset, with inference time reported next to accuracy. — For Mova (tree): Compare the perceptron parser and gparse on identical treebank splits, reporting LAS together with parse time, so the cost of extra accuracy is visible.
- The authors broke results down per letter to show that their method stays stable where the deep models collapse on certain classes. — For Mova (answer): Report test-reader scores per question type or per fable rather than one aggregate, so weak categories are not hidden by a good average.
- Label-preserving perturbations (noise, shifts, stretching) were used to multiply a tiny hand-recorded dataset. — For Mova (learn): Meaning-preserving text perturbations, such as name swaps or number changes in word problems, could enlarge small gold sets and also serve as consistency checks.

*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/)

## Cited by

Cited by

3 papers of our library cite it (of the 140884 whose bibliographies we read); 1 of them have a page here, 2 not yet.

- [Enhancing Quranic Learning: A Multimodal Deep Learning Approach for Arabic Phoneme Recognition](https://md.research.elamur.ai/free/en/papers/kucukmanisa-2025-enhancing-quranic-learning-multimodal-deep/)

## Cite

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

```bibtex
@misc{calik2023ensemble,
  title = {{An ensemble-based framework for mispronunciation detection of Arabic phonemes}},
  author = {Calik, Sukru Selim and Kucukmanisa, Ayhan and Kilimci, Zeynep Hilal},
  year = {2023},
  doi = {10.48550/arxiv.2301.01378},
  eprint = {2301.01378},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2301.01378}
}
```

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## References

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