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         <!-- Task Description -->
 
-        <h1>IqraEval: Quranic Mispronunciation Detection System</h1>
+        <h2>Task Description: Quranic Mispronunciation Detection System</h2>
 
-  <p>
-    <strong>IqraEval</strong> is an AI-powered framework designed to detect and provide detailed feedback on mispronunciations in Quranic recitations. 
-    Users read aloud vowelized Quranic verses; the system analyzes the recorded audio against reference phoneme sequences and highlights any deviations. 
-    Models are evaluated on the <strong>QuranMB</strong> dataset, which contains human‐annotated mispronunciations.
-  </p>
-
-  <div class="centered">
-    <img src="task.png" alt="System Overview" style="max-width:100%; height:auto;" />
-    <p><em>Figure: Overview of the Mispronunciation Detection Workflow</em></p>
-  </div>
-
-  <h2>1. Read the Verse</h2>
-  <p>
-    The user is shown a <strong>Reference Verse</strong> in Arabic script along with its corresponding <strong>Reference Phoneme Sequence</strong>.
-  </p>
-  <p><strong>Example:</strong></p>
-  <ul>
-    <li><strong>Arabic:</strong> إِنَّ الصَّفَا وَالْمَرْوَةَ مِنْ شَعَائِرِ اللَّهِ</li>
-    <li>
-      <strong>Phoneme:</strong> 
-      <code>&lt; i n n a SS A f aa w a l m a r w a t a m i n $ a E a a &lt; i r i l l a h i</code>
-    </li>
-  </ul>
-
-  <h2>2. Save Recording</h2>
-  <p>
-    The user recites the verse aloud; the system captures and stores the audio waveform for subsequent analysis.
-  </p>
-
-  <h2>3. Pronunciation Assessment</h2>
-  <p>
-    The stored audio is fed into a <strong>Mispronunciation Detection Model</strong>. 
-    This model aligns the user’s pronunciation with the <em>Reference Phoneme Sequence</em> to detect any mismatches.
-  </p>
-  <p><strong>Example of Mispronunciation:</strong></p>
-  <ul>
-    <li><strong>Reference Sequence:</strong> <code>... m i n $ a E a a &lt; i r i l l a h i</code></li>
-    <li><strong>User’s Pronunciation:</strong> <code>... m i n s a E a a &lt; i r u l l a h i</code></li>
-    <li>
-      <strong>Annotated Feedback:</strong> 
-      <code>... m i n <span class="highlight">s</span> a E a a &lt; i <span class="highlight">r u</span> l l a h i</code>
-    </li>
-  </ul>
-  <p>
-    In this case, the phoneme <code>$</code> was mispronounced as <code>s</code>, and <code>i</code> was mispronounced as <code>u</code>.
-  </p>
-
-  <h2>4. Feedback</h2>
-  <p>
-    The system generates an <strong>Annotated Verse</strong> and phoneme‐level feedback. 
-    Mispronunciations are highlighted (for example, in red) to help the user identify and correct their errors.
-  </p>
-
-  <h2>Research Directions</h2>
-  <ol>
-    <li>
-      <strong>Advanced Mispronunciation Detection Models</strong><br>
-      Apply state-of-the-art self-supervised models (e.g., 
-      <a href="https://arxiv.org/abs/2111.06331" target="_blank">Wav2Vec2.0</a>, HuBERT) 
-      pre-trained on Arabic speech. These models can be fine-tuned on Quranic recitations to improve phoneme-level accuracy.
-    </li>
-    <li>
-      <strong>Data Augmentation Strategies</strong><br>
-      Create synthetic mispronunciation examples using pipelines like 
-      <a href="https://arxiv.org/abs/2211.00923" target="_blank">SpeechBlender</a>. 
-      Augmenting limited Arabic/Quranic speech data helps mitigate data scarcity and improves model robustness.
-    </li>
-    <li>
-      <strong>Analysis of Common Mispronunciation Patterns</strong><br>
-      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.
-    </li>
-    <li>
-      <strong>Integration with Tajwīd Rules</strong><br>
-      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.
-    </li>
-    <li>
-      <strong>Adaptive Learning Paths</strong><br>
-      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.
-    </li>
-  </ol>
-
-  <h2>References</h2>
-  <ul>
-    <li>
-      El Kheir, Y., et al. 
-      "<a href="https://arxiv.org/abs/2211.00923" target="_blank">SpeechBlender: Speech Augmentation Framework for Mispronunciation Data Generation</a>," 
-      <em>arXiv preprint arXiv:2211.00923</em>, 2022.
-    </li>
-    <li>
-      Al Harere, A., & Al Jallad, K. 
-      "<a href="https://arxiv.org/abs/2305.06429" target="_blank">Mispronunciation Detection of Basic Quranic Recitation Rules using Deep Learning</a>," 
-      <em>arXiv preprint arXiv:2305.06429</em>, 2023.
-    </li>
-    <li>
-      Aly, S. A., et al. 
-      "<a href="https://arxiv.org/abs/2111.01136" target="_blank">ASMDD: Arabic Speech Mispronunciation Detection Dataset</a>," 
-      <em>arXiv preprint arXiv:2111.01136</em>, 2021.
-    </li>
-    <li>
-      Moustafa, A., & Aly, S. A. 
-      "<a href="https://arxiv.org/abs/2111.06331" target="_blank">Towards an Efficient Voice Identification Using Wav2Vec2.0 and HuBERT Based on the Quran Reciters Dataset</a>," 
-      <em>arXiv preprint arXiv:2111.06331</em>, 2021.
-    </li>
-  </ul>
+        <p>
+          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 <strong>QuranMB.v2</strong> dataset, which contains human‐annotated mispronunciations.
+        </p>
 
+        <div class="centered">
+          <img src="task.png" alt="System Overview" style="max-width:100%; height:auto;" />
+          <p><em>Figure: Overview of the Mispronunciation Detection Workflow</em></p>
+        </div>
+                    
+          <h3>1. Read the Verse</h3>
+          <p>
+            The user is shown a <strong>Reference Verse</strong> in Arabic script along with its corresponding <strong>Reference Phoneme Sequence</strong>.
+          </p>
+          <p><strong>Example:</strong></p>
+          <ul>
+            <li><strong>Arabic:</strong> إِنَّ الصَّفَا وَالْمَرْوَةَ مِنْ شَعَائِرِ اللَّهِ</li>
+            <li>
+              <strong>Phoneme:</strong> 
+              <code>&lt; i n n a SS A f aa w a l m a r w a t a m i n $ a E a a &lt; i r i l l a h i</code>
+            </li>
+          </ul>
+        
+          <h3>2. Save Recording</h3>
+          <p>
+            The user recites the verse aloud; the system captures and stores the audio waveform for subsequent analysis.
+          </p>
+        
+          <h3>3. Mispronunciation Detection</h3>
+          <p>
+            The stored audio is fed into a <strong>Mispronunciation Detection Model</strong>. 
+            This model predicts the phoneme sequence uttered by the speaker, which may contain mispronunciations.
+          </p>
+          <p><strong>Example of Mispronunciation:</strong></p>
+          <ul>
+            <li><strong>Reference Sequence:</strong> <code>... m i n $ a E a a &lt; i r i l l a h i</code></li>
+            <li><strong>User’s Pronunciation:</strong> <code>... m i n s a E a a &lt; i r u l l a h i</code></li>
+            <li>
+              <strong>Annotated Feedback:</strong> 
+              <code>... m i n <span class="highlight">s</span> a E a a &lt; i <span class="highlight">r u</span> l l a h i</code>
+            </li>
+          </ul>
+          <p>
+            In this case, the phoneme <code>$</code> was mispronounced as <code>s</code>, and <code>i</code> was mispronounced as <code>u</code>.
+          </p>
+        
+        
+          <h2>Research Directions</h2>
+          <ol>
+            <li>
+              <strong>Advanced Mispronunciation Detection Models</strong><br>
+              Apply state-of-the-art self-supervised models (e.g., 
+              <a href="https://arxiv.org/abs/2111.06331" target="_blank">Wav2Vec2.0</a>, HuBERT) 
+              pre-trained on Arabic speech. These models can be fine-tuned on Quranic recitations to improve phoneme-level accuracy.
+            </li>
+            <li>
+              <strong>Data Augmentation Strategies</strong><br>
+              Create synthetic mispronunciation examples using pipelines like 
+              <a href="https://arxiv.org/abs/2211.00923" target="_blank">SpeechBlender</a>. 
+              Augmenting limited Arabic/Quranic speech data helps mitigate data scarcity and improves model robustness.
+            </li>
+            <li>
+              <strong>Analysis of Common Mispronunciation Patterns</strong><br>
+              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.
+            </li>
+            <li>
+              <strong>Integration with Tajwīd Rules</strong><br>
+              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.
+            </li>
+            <li>
+              <strong>Adaptive Learning Paths</strong><br>
+              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.
+            </li>
+          </ol>
+        
+          <h2>References</h2>
+          <ul>
+            <li>
+              El Kheir, Y., et al. 
+              "<a href="https://arxiv.org/abs/2211.00923" target="_blank">SpeechBlender: Speech Augmentation Framework for Mispronunciation Data Generation</a>," 
+              <em>arXiv preprint arXiv:2211.00923</em>, 2022.
+            </li>
+            <li>
+              Al Harere, A., & Al Jallad, K. 
+              "<a href="https://arxiv.org/abs/2305.06429" target="_blank">Mispronunciation Detection of Basic Quranic Recitation Rules using Deep Learning</a>," 
+              <em>arXiv preprint arXiv:2305.06429</em>, 2023.
+            </li>
+            <li>
+              Aly, S. A., et al. 
+              "<a href="https://arxiv.org/abs/2111.01136" target="_blank">ASMDD: Arabic Speech Mispronunciation Detection Dataset</a>," 
+              <em>arXiv preprint arXiv:2111.01136</em>, 2021.
+            </li>
+            <li>
+              Moustafa, A., & Aly, S. A. 
+              "<a href="https://arxiv.org/abs/2111.06331" target="_blank">Towards an Efficient Voice Identification Using Wav2Vec2.0 and HuBERT Based on the Quran Reciters Dataset</a>," 
+              <em>arXiv preprint arXiv:2111.06331</em>, 2021.
+            </li>
+          </ul>
+                                                      
 
         <h2>🔊 Task Description</h2>
         <p>