Update index.html

index.html

@@ -223,15 +223,15 @@
           From these counts, we derive three rates:
           <ul>
             <li><strong>False Rejection Rate (FRR):</strong>  
-              \( \displaystyle \text{FRR} = \frac{\text{FR}}{\text{TA} + \text{FR}} \)  
+              FRR = FR/(TA + FR)  
               (Proportion of correctly pronounced phonemes that were mistakenly flagged as errors.)
             </li>
-            <li><strong>False Acceptance Rate (FAR):</strong>  
-              \( \displaystyle \text{FAR} = \frac{\text{FA}}{\text{FA} + \text{TR}} \)  
+            <li><strong>False Acceptance Rate (FAR):</strong> 
+              FAR = FA/(FA + TR)
               (Proportion of mispronounced phonemes that were mistakenly classified as correct.)
             </li>
-            <li><strong>Diagnostic Error Rate (DER):</strong>  
-              \( \displaystyle \text{DER} = \frac{\text{DE}}{\text{CD} + \text{DE}} \)  
+            <li><strong>Diagnostic Error Rate (DER):</strong>
+              DER = DE/(CD + DE)
               where DE is the number of misdiagnosed phonemes and CD is the number of correctly diagnosed ones.
             </li>
           </ul>
@@ -240,16 +240,15 @@
           In addition to these hierarchical measures, we compute the standard <strong>Precision</strong>, <strong>Recall</strong>, and <strong>F-measure</strong> for mispronunciation detection:
           <ul>
             <li><strong>Precision:</strong>  
-              \( \displaystyle \text{Precision} = \frac{\text{TR}}{\text{TR} + \text{FR}} \)  
+              Precision = TR/(TR + FR)
               (Of all phonemes predicted as mispronounced, how many were actually mispronounced?)
             </li>
             <li><strong>Recall:</strong>  
-              \( \displaystyle \text{Recall} = \frac{\text{TR}}{\text{TR} + \text{FA}} \;=\; 1 - \text{FAR} \)  
+              Recall = TR/(TR + FA)
               (Of all truly mispronounced phonemes, how many did we correctly detect?)
             </li>
-            <li><strong>F-measure (F1):</strong>  
-              \( \displaystyle F1 = 2 \times \frac{\text{Precision} \times \text{Recall}}{\text{Precision} + \text{Recall}} \)  
-              (Harmonic mean of Precision and Recall.)
+            <li><strong>F1-score:</strong> 
+              F1-score = 2 * Precision * Recall / (Precision + Recall)
             </li>
           </ul>
         </p>