pith. machine review for the scientific record. sign in

arxiv: 2603.29087 · v2 · submitted 2026-03-31 · 💻 cs.SD · eess.AS

Recognition: 1 theorem link

· Lean Theorem

IQRA 2026: Interspeech Challenge on Automatic Pronunciation Assessment for Modern Standard Arabic (MSA)

Yassine El Kheir , Amit Meghanani , Mostafa Shahin , Omnia Ibrahim , Shammur Absar Chowdhury , Nada AlMarwani , Youssef Elshahawy , Ahmed Ali
Authors on Pith no claims yet

Pith reviewed 2026-05-14 00:10 UTC · model grok-4.3

classification 💻 cs.SD eess.AS
keywords mispronunciation detectionModern Standard ArabicInterspeech challengepronunciation assessmentMDDauthentic datasetself-supervised learning
0
0 comments X

The pith

The second IQRA challenge shows a 0.28 F1-score gain in detecting mispronunciations in Modern Standard Arabic.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper reports results from the second edition of the IQRA Interspeech Challenge on automatic mispronunciation detection and diagnosis for Modern Standard Arabic. A new dataset of authentic human mispronounced speech was added to the training and evaluation resources. Participant systems using CTC-based self-supervised models, two-stage fine-tuning, and large audio-language models delivered a substantial performance increase over the first edition.

Core claim

The introduction of the Iqra_Extra_IS26 authentic mispronunciation dataset together with novel participant architectures and modeling strategies produced a 0.28 F1-score improvement in MDD for MSA compared with the prior challenge edition.

What carries the argument

The Iqra_Extra_IS26 dataset of authentic human mispronounced speech, which supplies real pronunciation error examples to improve model training and evaluation.

Load-bearing premise

The observed performance jump stems mainly from the new authentic dataset and participant-proposed methods rather than changes in evaluation protocol or data distribution.

What would settle it

Re-evaluating the top systems from this edition on the first edition's dataset and finding little or no F1 improvement.

read the original abstract

We present the findings of the second edition of the IQRA Interspeech Challenge, a challenge on automatic Mispronunciation Detection and Diagnosis (MDD) for Modern Standard Arabic (MSA). Building on the previous edition, this iteration introduces \textbf{Iqra\_Extra\_IS26}, a new dataset of authentic human mispronounced speech, complementing the existing training and evaluation resources. Submitted systems employed a diverse range of approaches, spanning CTC-based self-supervised learning models, two-stage fine-tuning strategies, and using large audio-language models. Compared to the first edition, we observe a substantial jump of \textbf{0.28 in F1-score}, attributable both to novel architectures and modeling strategies proposed by participants and to the additional authentic mispronunciation data made available. These results demonstrate the growing maturity of Arabic MDD research and establish a stronger foundation for future work in Arabic pronunciation assessment.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The manuscript reports findings from the second edition of the IQRA Interspeech Challenge on automatic mispronunciation detection and diagnosis (MDD) for Modern Standard Arabic (MSA). It introduces the new Iqra_Extra_IS26 dataset of authentic human mispronounced speech to complement existing resources and states that submitted systems achieved a 0.28 F1-score improvement over the first edition, attributing this gain to novel participant architectures (e.g., CTC-based self-supervised models, two-stage fine-tuning, large audio-language models) and the additional authentic data.

Significance. If the reported improvement and its attribution can be substantiated, the work demonstrates meaningful progress in Arabic MDD by expanding resources with authentic mispronunciations and showcasing diverse modeling strategies. This could provide a stronger empirical foundation for future pronunciation assessment research in under-resourced languages. However, the absence of controlled comparisons limits the ability to isolate the contributions of the new data versus other factors, reducing the immediate impact on the field.

major comments (1)
  1. Abstract: The central claim of a 0.28 F1-score jump 'attributable both to novel architectures and modeling strategies proposed by participants and to the additional authentic mispronunciation data' is not supported by any cross-edition baseline, ablation, or controlled experiment. No re-evaluation of prior top systems on the new test set, no isolation of the Iqra_Extra_IS26 data effect while holding models fixed, and no discussion of possible changes in test-set composition, labeling criteria, or F1 computation protocol are provided, so the delta cannot be confidently attributed to the claimed factors rather than distribution or evaluation shifts.
minor comments (2)
  1. The manuscript would benefit from explicit reporting of the exact F1-score computation details, data splits used for the new dataset, statistical significance tests on the improvement, and per-system performance tables to allow readers to assess the diversity and robustness of submitted approaches.
  2. Error analysis or qualitative examples of remaining mispronunciation types that the new systems still struggle with would strengthen the discussion of growing maturity in Arabic MDD.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript for the IQRA 2026 challenge. We address the major comment on the attribution of the observed F1-score improvement below and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: Abstract: The central claim of a 0.28 F1-score jump 'attributable both to novel architectures and modeling strategies proposed by participants and to the additional authentic mispronunciation data' is not supported by any cross-edition baseline, ablation, or controlled experiment. No re-evaluation of prior top systems on the new test set, no isolation of the Iqra_Extra_IS26 data effect while holding models fixed, and no discussion of possible changes in test-set composition, labeling criteria, or F1 computation protocol are provided, so the delta cannot be confidently attributed to the claimed factors rather than distribution or evaluation shifts.

    Authors: We agree that the manuscript does not include controlled ablations, re-evaluations of prior systems on the updated test set, or explicit analysis of potential shifts in test-set composition, labeling, or F1 protocol. The reported 0.28 F1 improvement is an observed difference between challenge editions, and while the new authentic data and advanced participant systems were introduced this year, we cannot isolate their individual contributions or rule out confounding factors. We will revise the abstract to report the observed gain without claiming direct attribution, instead noting that it coincides with the availability of Iqra_Extra_IS26 and the diverse modeling approaches. We will also add a limitations paragraph discussing possible evaluation shifts and recommending future controlled experiments to isolate data versus architecture effects. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical challenge report with direct observations only

full rationale

The paper is an Interspeech challenge summary that reports participant-submitted system results and an observed 0.28 F1-score increase relative to the prior edition. No derivations, equations, predictions, or parameter fits are present. The attribution statement is an observational claim about submitted systems and added data, with no self-referential reduction, fitted-input-as-prediction, or load-bearing self-citation chain that collapses the result to its own inputs by construction. The derivation chain is therefore empty and self-contained as raw empirical reporting.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is an empirical challenge summary paper with no mathematical derivations, fitted parameters, background axioms, or newly postulated entities.

pith-pipeline@v0.9.0 · 5488 in / 1216 out tokens · 62988 ms · 2026-05-14T00:10:52.043869+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

43 extracted references · 43 canonical work pages · 1 internal anchor

  1. [1]

    Introduction The field of Computer-Aided Pronunciation Training (CAPT) and its core component, Mispronunciation Detection and Diag- nosis (MDD), has become indispensable tools for self-directed language learners globally [1, 2]. CAPT systems serve two primary functions: (i) pronunciation assessment, identifying and localising phoneme-level errors in a lea...

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  2. [2]

    Task Definition The task follows [21, 20]: given a speech utterance and its ref- erence vowelized transcript, the systems predict the sequence of pronounced phonemes

    Challenge Setup 2.1. Task Definition The task follows [21, 20]: given a speech utterance and its ref- erence vowelized transcript, the systems predict the sequence of pronounced phonemes. Predictions are aligned against both the canonical phoneme sequence and the annotated verbatim sequence (the deliberately mispronounced version) to derive MDD metrics. W...

    work page

  3. [3]

    Expert in- house vowelization was applied to all transcriptions, followed by phonetization via the Halabi MSA phonetizer

    augmented with Qur’anic recitation segments. Expert in- house vowelization was applied to all transcriptions, followed by phonetization via the Halabi MSA phonetizer. The resulting 74,000 utterances span diverse speakers with balanced gender distribution. Unlike the internal CMV-Ar corpus used in the first edition, Iqra train is now publicly accessible, e...

    work page 2026

  4. [4]

    Training uses Adam (lr=10−4), batch size 16, and early stopping on dev-set PER, with Iqra train and Iqra TTS as training data

    Baseline System We provide a single SSL-based baseline following the setup in [21]: the multilingualmHuBERT[24] model (pretrained on 147 languages, 94M parameters) with a frozen encoder, a SUPERB-style [25] weighted layer sum, and a 2-layer 1024- unit Bi-LSTM with CTC loss [26]. Training uses Adam (lr=10−4), batch size 16, and early stopping on dev-set PE...

    work page

  5. [5]

    Submitted Systems We summarize the top-6 systems ranked by F1-score on QuranMB.v2. The submissions collectively span three broad paradigms: enhanced CTC-based temporal modeling, SSL fine- tuning with language model integration, and generative large audio-language models (LALMs). whu-iasp (1st, F1=0.7201).This system combines a frozen wav2vec2-xls-r-300m1 ...

    work page arXiv 1918

  6. [6]

    Overall Performance Table 2 presents the full leaderboard on QuranMB.v2

    Results and Discussion 5.1. Overall Performance Table 2 presents the full leaderboard on QuranMB.v2. The results demonstrate broad and substantial community-wide progress: 13 of 19 submitted systems surpass the organizer baseline (F1=0.4414), with the best-performing system (whu- iasp) achieving an absolute F1 improvement of0.2787over the baseline. Notabl...

    work page 2025

  7. [7]

    A recurring theme across top submissions is the disproportionate value of authen- tic mispronunciation data

    Discussion The IQRA 2026 challenge marks a significant milestone in Arabic pronunciation assessment, yet the results also surface important open questions that the community must address to move this research toward real-world impact. A recurring theme across top submissions is the disproportionate value of authen- tic mispronunciation data. DespiteIqra E...

    work page 2026

  8. [8]

    Conclusion We have presented IQRA 2026, the challenge introduced Iqra Extra IS26, the first corpus of authentic human mispro- nounced MSA speech, alongside the expanded QuranMB.v2 evaluation benchmark, and attracted 19 teams, nearly double previous editions. Submitted systems dramatically surpassed the organizer baseline, with the best system achieving an...

    work page 2026

  9. [9]

    The effective- ness of computer assisted pronunciation training for foreign lan- guage learning by children,

    A. Neri, O. Mich, M. Gerosa, and D. Giuliani, “The effective- ness of computer assisted pronunciation training for foreign lan- guage learning by children,”Computer Assisted Language Learn- ing, vol. 21, no. 5, pp. 393–408, 2008

    work page 2008

  10. [10]

    Computer-assisted pronunciation train- ing (capt): Current issues and future directions,

    P. M. Rogerson-Revell, “Computer-assisted pronunciation train- ing (capt): Current issues and future directions,”Relc Journal, vol. 52, no. 1, pp. 189–205, 2021

    work page 2021

  11. [11]

    Automatic pronun- ciation assessment–a review,

    Y . E. Kheir, A. Ali, and S. A. Chowdhury, “Automatic pronun- ciation assessment–a review,”arXiv preprint arXiv:2310.13974, 2023

    work page arXiv 2023

  12. [12]

    Mispronunciation detection and diagnosis in l2 english speech using multidistribution deep neural networks,

    K. Li, X. Qian, and H. Meng, “Mispronunciation detection and diagnosis in l2 english speech using multidistribution deep neural networks,”IEEE/ACM Transactions on Audio, Speech, and Lan- guage Processing, vol. 25, no. 1, pp. 193–207, 2016

    work page 2016

  13. [13]

    Cnn-rnn-ctc based end-to- end mispronunciation detection and diagnosis,

    W.-K. Leung, X. Liu, and H. Meng, “Cnn-rnn-ctc based end-to- end mispronunciation detection and diagnosis,” inICASSP 2019- 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019, pp. 8132–8136

    work page 2019

  14. [14]

    Capturing L2 segmental mis- pronunciations with joint-sequence models in CAPT,

    X. Qian, H. Meng, and F. Soong, “Capturing L2 segmental mis- pronunciations with joint-sequence models in CAPT,” inProc. In- terspeech, 2010

    work page 2010

  15. [15]

    Evaluating Wav2Vec2-BERT for CAPT in low- resource languages,

    S. Fortet al., “Evaluating Wav2Vec2-BERT for CAPT in low- resource languages,” inProc. Interspeech, 2025

    work page 2025

  16. [16]

    Qvoice: Arabic speech pronunciation learning appli- cation,

    Y . E. Kheir, F. Khnaisser, S. A. Chowdhury, H. Mubarak, S. Afzal, and A. Ali, “Qvoice: Arabic speech pronunciation learning appli- cation,”arXiv preprint arXiv:2305.07445, 2023

    work page arXiv 2023

  17. [17]

    Beyond orthography: Automatic recovery of short vowels and dialectal sounds in arabic,

    Y . E. Kheir, H. Mubarak, A. Ali, and S. A. Chowdhury, “Beyond orthography: Automatic recovery of short vowels and dialectal sounds in arabic,”arXiv preprint arXiv:2408.02430, 2024

    work page arXiv 2024

  18. [18]

    Improving mis- pronunciation detection and diagnosis for non-native learners of the arabic language,

    N. Alrashoudi, H. Al-Khalifa, and Y . Alotaibi, “Improving mis- pronunciation detection and diagnosis for non-native learners of the arabic language,”Discover Computing, vol. 28, no. 1, p. 1, 2025

    work page 2025

  19. [19]

    Speechocean762: An open- source non-native English speech corpus for pronunciation assess- ment,

    J. Zhang, C. Ni, S. Zhanget al., “Speechocean762: An open- source non-native English speech corpus for pronunciation assess- ment,” inProc. Interspeech, 2021

    work page 2021

  20. [20]

    Phonetic RNN- Transducer for mispronunciation detection,

    D. Y . Zhang, S. Saha, and S. Campbell, “Phonetic RNN- Transducer for mispronunciation detection,” inProc. ICASSP, 2023

    work page 2023

  21. [21]

    A novel frame- work for mispronunciation detection of arabic phonemes using audio-oriented transformer models,

    S ¸. S. C ¸ alık, A. K¨uc ¸¨ukmanisa, and Z. H. Kilimci, “A novel frame- work for mispronunciation detection of arabic phonemes using audio-oriented transformer models,”Applied Acoustics, vol. 215, p. 109711, 2024

    work page 2024

  22. [22]

    wav2vec 2.0: A framework for self-supervised learning of speech repre- sentations,

    A. Baevski, Y . Zhou, A. Mohamed, and M. Auli, “wav2vec 2.0: A framework for self-supervised learning of speech repre- sentations,”Advances in neural information processing systems, vol. 33, pp. 12 449–12 460, 2020

    work page 2020

  23. [23]

    Hubert: Self-supervised speech representation learning by masked prediction of hidden units,

    W. Hsu, B. Bolte, Y . Tsai, K. Lakhotia, R. Salakhutdinov, and A. Mohamed, “Hubert: Self-supervised speech representation learning by masked prediction of hidden units,”IEEE/ACM Trans. Audio, Speech and Lang. Proc., vol. 29, p. 3451–3460, oct

    work page

  24. [24]

    Available: https://doi.org/10.1109/TASLP.2021

    [Online]. Available: https://doi.org/10.1109/TASLP.2021. 3122291

    work page doi:10.1109/taslp.2021 2021

  25. [25]

    Automatic pronunciation assessment using self- supervised speech representation learning,

    S. Kimet al., “Automatic pronunciation assessment using self- supervised speech representation learning,” inProc. Interspeech, 2022

    work page 2022

  26. [26]

    Assessment of non- native speech intelligibility using Wav2vec2-based MDD and multi-level GOP transformer,

    R. C. Shekar, M. Yang, K. Hirschiet al., “Assessment of non- native speech intelligibility using Wav2vec2-based MDD and multi-level GOP transformer,” inProc. Interspeech, 2023

    work page 2023

  27. [27]

    The mgb-2 challenge: Arabic multi-dialect broadcast media recognition,

    A. Ali, P. Bell, J. Glass, Y . Messaoui, H. Mubarak, S. Renals, and Y . Zhang, “The mgb-2 challenge: Arabic multi-dialect broadcast media recognition,” in2016 IEEE Spoken Language Technology Workshop (SLT). IEEE, 2016, pp. 279–284

    work page 2016

  28. [28]

    Non-native children’s automatic speech assess- ment challenge (NOCASA),

    P. Gerberet al., “Non-native children’s automatic speech assess- ment challenge (NOCASA),” inProc. IEEE MLSP, 2025

    work page 2025

  29. [29]

    Iqra’eval: A shared task on qur’anic pronunciation assessment,

    Y . El Kheir, A. Meghanani, H. O. Toyin, N. Almarwani, O. Ibrahim, Y . A. Elshahawy, M. Shahin, and A. Ali, “Iqra’eval: A shared task on qur’anic pronunciation assessment,” inProceed- ings of The Third Arabic Natural Language Processing Confer- ence: Shared Tasks, 2025, pp. 443–452

    work page 2025

  30. [30]

    Towards a Uni- fied Benchmark for Arabic Pronunciation Assessment: Qur’anic Recitation as Case Study,

    Y . El Kheir, O. Ibrahim, A. Meghanani, N. Almarwani, H. Toyin, S. Alharbi, M. Alfadly, L. Alkanhal, I. Selim, S. Elbatal, S. Md- haffar, T. Hain, Y . Hifny, M. Shahin, and A. Ali, “Towards a Uni- fied Benchmark for Arabic Pronunciation Assessment: Qur’anic Recitation as Case Study,” inInterspeech 2025, 2025, pp. 2410– 2414

    work page 2025

  31. [31]

    Phonetic inventory for an arabic speech corpus,

    N. Halabi and M. Wald, “Phonetic inventory for an arabic speech corpus,” inProceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16), 2016, pp. 734– 738

    work page 2016

  32. [32]

    Common V oice: A massively- multilingual speech corpus

    R. Ardila, M. Branson, K. Davis, M. Henretty, M. Kohler, J. Meyer, R. Morais, L. Saunders, F. M. Tyers, and G. Weber, “Common voice: A massively-multilingual speech corpus,”arXiv preprint arXiv:1912.06670, 2019

    work page arXiv 1912

  33. [33]

    mhubert-147: A compact multilingual hubert model,

    M. Zanon Boito, V . Iyer, N. Lagos, L. Besacier, and I. Calapode- scu, “mhubert-147: A compact multilingual hubert model,” inIn- terspeech 2024, 2024, pp. 3939–3943

    work page 2024

  34. [34]

    Superb: Speech processing universal performance benchmark,

    S. wen Yang, P.-H. Chi, Y .-S. Chuang, C.-I. J. Lai, and K. L. et al., “Superb: Speech processing universal performance benchmark,” inInterspeech 2021, 2021, pp. 1194–1198

    work page 2021

  35. [35]

    Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks,

    A. Graves, S. Fern ´andez, F. Gomez, and J. Schmidhuber, “Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks,” inICML, ser. ICML ’06. NY , USA: Association for Computing Machinery, 2006, p. 369–376. [Online]. Available: https: //doi.org/10.1145/1143844.1143891

    work page doi:10.1145/1143844.1143891 2006

  36. [36]

    Tem- poral convolutional networks for action segmentation and detec- tion,

    C. Lea, M. D. Flynn, R. Vidal, A. Reiter, and G. D. Hager, “Tem- poral convolutional networks for action segmentation and detec- tion,” inproceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 156–165

    work page 2017

  37. [37]

    Modified kneser-ney smoothing of n-gram models,

    F. James, “Modified kneser-ney smoothing of n-gram models,” Research Institute for Advanced Computer Science, Tech. Rep. 00.07, 2000

    work page 2000

  38. [38]

    wav2vec 2.0: A framework for self-supervised learning of speech representa- tions,

    A. Baevski, Y . Zhou, A. Mohamed, and M. Auli, “wav2vec 2.0: A framework for self-supervised learning of speech representa- tions,” inAdvances in Neural Information Processing Systems, H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, and H. Lin, Eds., vol. 33. Curran Associates, Inc., 2020, pp. 12 449–12 460

    work page 2020

  39. [39]

    U2++: Unified two-pass bidirectional end-to-end model for speech recognition,

    D. Wu, B. Zhang, C. Yang, Z. Peng, W. Xia, X. Chen, and X. Lei, “U2++: Unified two-pass bidirectional end-to-end model for speech recognition,”arXiv preprint arXiv:2106.05642, 2021

    work page arXiv 2021

  40. [40]

    Conditional variational autoencoder with adversarial learning for end-to-end text-to-speech,

    J. Kim, J. Kong, and J. Son, “Conditional variational autoencoder with adversarial learning for end-to-end text-to-speech,” 2021. [Online]. Available: https://arxiv.org/abs/2106.06103

    work page arXiv 2021

  41. [41]

    Specaugment: A simple data augmen- tation method for automatic speech recognition,

    D. S. Park, W. Chan, Y . Zhang, C.-C. Chiu, B. Zoph, E. D. Cubuk, and Q. V . Le, “Specaugment: A simple data augmen- tation method for automatic speech recognition,”arXiv preprint arXiv:1904.08779, 2019

    work page arXiv 1904

  42. [42]

    Fleurs: Few-shot learning evaluation of universal representations of speech,

    A. Conneau, M. Ma, S. Khanuja, Y . Zhang, V . Axelrod, S. Dalmia, J. Riesa, C. Rivera, and A. Bapna, “Fleurs: Few-shot learning evaluation of universal representations of speech,” in2022 IEEE Spoken Language Technology Workshop (SLT). IEEE, 2023, pp. 798–805

    work page 2023

  43. [43]

    Com- mon voice: A massively-multilingual speech corpus,

    R. Ardila, M. Branson, K. Davis, M. Kohler, J. Meyer, M. Hen- retty, R. Morais, L. Saunders, F. Tyers, and G. Weber, “Com- mon voice: A massively-multilingual speech corpus,” inProceed- ings of the twelfth language resources and evaluation conference, 2020, pp. 4218–4222

    work page 2020