Update index.html

index.html

@@ -258,12 +258,31 @@ ID,Labels
       </ul>
     </p>
 
-    <h2>Potential Research Directions</h2>
+    <h2>Suggested Research Directions</h2>
+          <ol>
+            <li>
+              <strong>Advanced Mispronunciation Detection Models</strong><br>
+              Apply state-of-the-art self-supervised models (e.g., Wav2Vec2.0, HuBERT), using variants that are pre-trained/fine-tuned on Arabic speech. These models can then 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>
+          </ol> 
+
+    <!-- <h2>Suggested Research Directions</h2>
     <ol>
       <li><strong>Advanced Models:</strong> fine-tune Wav2Vec2.0, HuBERT on Arabic/Quranic speech.</li>
       <li><strong>Data Augmentation:</strong> use SpeechBlender to synthesize mispronunciations.</li>
       <li><strong>Pattern Analysis:</strong> statistical study of QuranMB errors to guide training.</li>
-    </ol>
+    </ol> -->
 
     <h2>Registration</h2>
     <p>