App: use the paper's exact table captions

app.py

@@ -455,7 +455,7 @@ Accuracy = % of reference boundaries matched within the ms tolerance. **Speciali
 trained on the target English corpus; **joint** = one model jointly trained on
 TIMIT+Buckeye; multilingual rows are **zero-shot** (no target-language training data).
 
-**Phone-level — English**
+**Phone-level Alignment Accuracy [%]: MFA vs. FALCON (Ours)**
 
 | Dataset | Model | t≤10 | t≤25 | t≤50 | t≤100 |
 |---|---|---|---|---|---|
@@ -466,7 +466,7 @@ TIMIT+Buckeye; multilingual rows are **zero-shot** (no target-language training
 | Buckeye | FALCON specialist | 29.69 | **69.93** | **90.07** | **97.40** |
 | Buckeye | FALCON joint | 28.87 | 69.40 | 89.53 | 97.13 |
 
-**Phoneme-level — zero-shot multilingual**
+**Phoneme-Level: Unseen Multilingual Generalization Accuracy**
 
 | Test set | Model | ≤10 | ≤15 | ≤20 | ≤25 | ≤50 | ≤100 |
 |---|---|---|---|---|---|---|---|
@@ -479,7 +479,7 @@ TIMIT+Buckeye; multilingual rows are **zero-shot** (no target-language training
 | Hebrew | **FALCON joint** | **21.98** | **30.10** | **36.91** | **42.78** | **63.07** | **80.41** |
 | Hebrew | FALCON specialist | 21.03 | 27.78 | 34.30 | 39.79 | 59.38 | 77.76 |
 
-**Word-level — English**
+**Word-Level Alignment Accuracy [%]: Comparative Analysis**
 
 | Dataset | Model | t≤10 | t≤25 | t≤50 | t≤100 |
 |---|---|---|---|---|---|
@@ -496,7 +496,7 @@ TIMIT+Buckeye; multilingual rows are **zero-shot** (no target-language training
 | Buckeye | WhisperX | 18.80 | 43.10 | 67.40 | 77.40 |
 | Buckeye | Nvidia-Canary-1b | 8.06 | 18.83 | 36.31 | 63.29 |
 
-**Word-level — zero-shot multilingual**
+**Word-Level: Unseen Multilingual Generalization Accuracy**
 
 | Dataset | Model | t≤10 | t≤25 | t≤50 | t≤100 |
 |---|---|---|---|---|---|