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<title>Iqra’Eval Shared Task</title>
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<h1>Iqra’Eval Shared Task</h1>
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<img src="IqraEval.png" alt="" style="max-width:100%; height:auto;" />
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<!-- Overview Section -->
<h2>Overview</h2>
<p>
<strong>Iqra’Eval</strong> is a shared task aimed at advancing <strong>automatic assessment of Qur’anic recitation pronunciation</strong> 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.
</p>
<p>
Participants will develop systems capable of:
</p>
<ul>
<li>Detecting whether a segment of Qur’anic recitation contains pronunciation errors.</li>
<li>Diagnosing the nature of the error (e.g., substitution, deletion, or insertion of phonemes).</li>
</ul>
<!-- Timeline Section -->
<h2>Timeline</h2>
<ul>
<li><strong>June 1, 2025</strong>: Official announcement of the shared task</li>
<li><strong>June 8, 2025</strong>: Release of training data, development set (QuranMB), phonetizer script, and baseline systems</li>
<li><strong>July 24, 2025</strong>: Registration deadline and release of test data</li>
<li><strong>July 27, 2025</strong>: End of evaluation cycle (test set submission closes)</li>
<li><strong>July 30, 2025</strong>: Final results released</li>
<li><strong>August 15, 2025</strong>: System description paper submissions due</li>
<li><strong>August 22, 2025</strong>: Notification of acceptance</li>
<li><strong>September 5, 2025</strong>: Camera-ready versions due</li>
</ul>
<!-- Task Description -->
<h2>🔊 Task Description</h2>
<p>
The Iqra'Eval task focuses on <strong>automatic pronunciation assessment</strong> in Qur’anic context.
Given a spoken audio clip of a verse and its fully vowelized reference text, your system should predict
the <strong>correct phoneme sequence</strong> actually spoken by the reciter.
</p>
<p>
By comparing this predicted sequence to the reference text and the gold phoneme sequence annotation, we can automatically detect pronunciation issues, such as:
</p>
<ul>
<li><strong>Substitutions</strong>: e.g., saying /k/ instead of /q/</li>
<li><strong>Insertions</strong>: adding a sound not present in the reference</li>
<li><strong>Deletions</strong>: skipping a required phoneme</li>
</ul>
<p>
This task helps diagnose and localize pronunciation errors, enabling educational feedback in applications like Qur’anic tutoring or speech evaluation tools.
</p>
<h2>Dataset Description</h2>
<p>
All data are hosted on Hugging Face. Two main splits are provided:
</p>
<ul>
<li>
<strong>Training set:</strong> 79 hours of Modern Standard Arabic (MSA) speech, augmented with multiple Qur’anic recitations.
<br />
<code>df = load_dataset("IqraEval/Iqra_train", split="train")</code>
</li>
<li>
<strong>Development set:</strong> 3.4 hours reserved for tuning and validation.
<br />
<code>df = load_dataset("IqraEval/Iqra_train", split="dev")</code>
</li>
</ul>
<p>
<strong>Column Definitions:</strong>
</p>
<ul>
<li><code>audio</code>: Speech Array.</li>
<li><code>sentence</code>: Original sentence text (may be partially diacritized or non-diacritized).</li>
<li><code>index</code>: If from the Quran, the verse index (0–6265, including Basmalah); otherwise <code>-1</code>.</li>
<li><code>tashkeel_sentence</code>: Fully diacritized sentence (auto-generated via a diacritization tool).</li>
<li><code>phoneme</code>: Phoneme sequence corresponding to the diacritized sentence (Nawar Halabi phonetizer).</li>
</ul>
<p>
<strong>Data Splits:</strong>
<br />
• Training (train): 79 hours total<br />
• Development (dev): 3.4 hours total
</p>
<!-- Additional TTS Data -->
<h2>TTS Data (Optional Use)</h2>
<p>
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:
</p>
<ul>
<li><code>df_tts = load_dataset("IqraEval/Iqra_TTS")</code></li>
</ul>
<h2>Test Data QuranMB</h2>
<p>
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.
</p>
<ul>
<li><code>df_test = load_dataset("IqraEval/Iqra_QuranMB_v2")</code></li>
</ul>
<!-- Resources & Links -->
<h2>Resources</h2>
<ul>
<li>
<a href="https://huggingface.co/datasets/IqraEval/Iqra_train" target="_blank">
Training & Development Data on Hugging Face
</a>
</li>
<li>
<a href="https://huggingface.co/datasets/IqraEval/Iqra_TTS" target="_blank">
IqraEval TTS Data on Hugging Face
</a>
</li>
<li>
<a href="https://github.com/Iqra-Eval/interspeech_IqraEval" target="_blank">
Baseline systems & training scripts (GitHub)
</a>
</li>
</ul>
<p>
<em>
For detailed instructions on data access, phonetizer installation, and baseline usage, please refer to the GitHub README.
</em>
</p>
<h2>Evaluation Criteria</h2>
<p>
Systems will be scored on their ability to detect and correctly classify phoneme-level errors:
</p>
<ul>
<li><strong>Detection accuracy:</strong> Did the system spot that a phoneme-level error occurred in the segment?</li>
<li><strong>Classification F1-score:</strong> Mispronunciation Detection F1-score</li>
</ul>
<p>
<em>(Detailed evaluation weights and scripts will be made available on June 5, 2025.)</em>
</p>
<!-- Submission Details -->
<h2>Submission Details (Draft)</h2>
<p>
Participants are required to submit a CSV file named <code>submission.csv</code> containing the predicted phoneme sequences for each audio sample. The file must have exactly two columns:
</p>
<ul>
<li><strong>ID:</strong> Unique identifier of the audio sample.</li>
<li><strong>Labels:</strong> The predicted phoneme sequence, with each phoneme separated by a single space.</li>
</ul>
<p>
Below is a minimal example illustrating the required format:
</p>
<pre>
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
…
</pre>
<p>
The first column (ID) should match exactly the audio filenames (without extension). The second column (Labels) is the predicted phoneme string.
</p>
<p>
<strong>Important:</strong>
<ul>
<li>Use UTF-8 encoding.</li>
<li>Do not include extra spaces at the start or end of each line.</li>
<li>Submit a single CSV file (no archives). Filename must be <code>submission.csv</code>.</li>
</ul>
</p>
<!-- Placeholder for Future Details -->
<h2>Future Updates</h2>
<p>
Further details on <strong>evaluation criteria</strong> (exact scoring weights), <strong>submission templates</strong>, and any clarifications will be posted on the shared task website when test data are released (June 5, 2025). Stay tuned!
</p>
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