Iqra’Eval Shared Task

Overview

Iqra’Eval is a shared task aimed at advancing automatic assessment of Qur’anic recitation pronunciation by leveraging computational methods to detect and diagnose pronunciation errors. The focus on Qur’anic recitation provides a standardized and well-defined context for evaluating Modern Standard Arabic (MSA) pronunciation, where precise articulation is not only valued but essential for correctness according to established Tajweed rules.

Participants will develop systems capable of:

Timeline

Task Description: Quranic Mispronunciation Detection System

The aim is to design a model to detect and provide detailed feedback on mispronunciations in Quranic recitations. Users read aloud vowelized Quranic verses; This model predicts the phoneme sequence uttered by the speaker, which may contain mispronunciations. Models are evaluated on the QuranMB.v2 dataset, which contains human‐annotated mispronunciations.

System Overview

Figure: Overview of the Mispronunciation Detection Workflow

1. Read the Verse

The user is shown a Reference Verse in Arabic script along with its corresponding Reference Phoneme Sequence.

Example:

2. Save Recording

The user recites the verse aloud; the system captures and stores the audio waveform for subsequent analysis.

3. Mispronunciation Detection

The stored audio is fed into a Mispronunciation Detection Model. This model predicts the phoneme sequence uttered by the speaker, which may contain mispronunciations.

Example of Mispronunciation:

In this case, the phoneme $ was mispronounced as s, and i was mispronounced as u.

Research Directions

  1. Advanced Mispronunciation Detection Models
    Apply state-of-the-art self-supervised models (e.g., Wav2Vec2.0, HuBERT) pre-trained on Arabic speech. These models can be fine-tuned on Quranic recitations to improve phoneme-level accuracy.
  2. Data Augmentation Strategies
    Create synthetic mispronunciation examples using pipelines like SpeechBlender. Augmenting limited Arabic/Quranic speech data helps mitigate data scarcity and improves model robustness.
  3. Analysis of Common Mispronunciation Patterns
    Perform statistical analysis on the QuranMB dataset to identify prevalent errors (e.g., substituting similar phonemes, swapping vowels). These insights can drive targeted training and tailored feedback rules.
  4. Integration with Tajwīd Rules
    Incorporate classical Tajwīd rules (e.g., madd, qalqalah, ikhfa͑) into the detection pipeline so that feedback not only flags errors but also explains the correct recitation rule.
  5. Adaptive Learning Paths
    Design a system that adapts the sequence of verses based on each user’s error patterns—focusing on the next set of verses that emphasize their weak phonemes.

References

Dataset Description

All data are hosted on Hugging Face. Two main splits are provided:

Column Definitions:

Data Splits:
• Training (train): 79 hours total
• Development (dev): 3.4 hours total

TTS Data (Optional Use)

We also provide a high-quality TTS corpus for auxiliary experiments (e.g., data augmentation, synthetic pronunciation error simulation). This TTS set can be loaded via:

Test Data QuranMB

To construct a reliable test set, we select 98 verses from the Qur’an, which are read aloud by 18 native Arabic speakers (14 females, 4 males), resulting in approximately 2 hours of recorded speech. The speakers were instructed to read the text in MSA at their normal tempo, disregarding Qur’anic tajweed rules, while deliberately producing the specified pronunciation errors. To ensure consistency in error production, we developed a custom recording tool that highlighted the modified text and displayed additional instructions specifying the type of error. Before recording, speakers were required to silently read each sentence to familiarize themselves with the intended errors before reading them aloud. After recording, three linguistic annotators verified and corrected the phoneme sequence and flagged all pronunciation errors for evaluation.

Resources

For detailed instructions on data access, phonetizer installation, and baseline usage, please refer to the GitHub README.

Evaluation Criteria

Systems will be scored on their ability to detect and correctly classify phoneme-level errors:

(Detailed evaluation weights and scripts will be made available on June 5, 2025.)

Submission Details (Draft)

Participants are required to submit a CSV file named submission.csv containing the predicted phoneme sequences for each audio sample. The file must have exactly two columns:

Below is a minimal example illustrating the required format: 

ID,Labels
0000_0001, i n n a m a a y a k h a l l a h a m i n ʕ i b a a d i h u l ʕ u l a m
0000_0002, m a a n a n s a k h u m i n i ʕ a a y a t i n
0000_0003, y u k h i k u m u n n u ʔ a u ʔ a m a n a t a n m m i n h u
…

The first column (ID) should match exactly the audio filenames (without extension). The second column (Labels) is the predicted phoneme string.

Important:

Future Updates

Further details on evaluation criteria (exact scoring weights), submission templates, and any clarifications will be posted on the shared task website when test data are released (June 5, 2025). Stay tuned!