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127b976 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 | # GLOBE Subset Patch
This document explains the GLOBE dataset subset workflow used in AuralGuard-AASIST++.
---
## 1. Why GLOBE Was Added
After training the balanced accent model, the system performed well on DECTE, EdAcc, and English Dialects.
However, testing on GLOBE exposed a new weakness: the model falsely flagged many real global-accent clips as fake.
This showed that even after adding some accent data, the model still needed broader global-accent exposure.
---
## 2. Problem
GLOBE is a large dataset. Downloading the full dataset is unnecessary for a small project experiment and may take too much storage and time.
The solution was to stream a small subset instead of downloading everything.
---
## 3. Goal of the Patch
The GLOBE subset patch creates a small local subset:
```text
1000 training clips
200 validation clips
about 198 test clips
```
This allows the model to learn from real global-accent English speech without downloading the full dataset.
---
## 4. Main Script
```text
scripts/make_globe_streaming_subset.py
```
Example command:
```cmd
python scripts\make_globe_streaming_subset.py --max-train 1000 --max-val 200 --max-test 200
```
Expected outputs:
```text
data\metadata\globe_train.csv
data\metadata\globe_val.csv
data\metadata\globe_test.csv
```
---
## 5. Important Metadata Check
After creating the CSV files, confirm that the GLOBE test set contains only GLOBE real speech and not accidentally mixed fake files.
```cmd
python -c "import pandas as pd; df=pd.read_csv('data\\metadata\\globe_test.csv'); print('WaveFake paths:', df.file_path.str.contains('wavefake|generated_audio', case=False).sum()); print('ASVspoof paths:', df.file_path.str.contains('asvspoof', case=False).sum())"
```
Expected result:
```text
WaveFake paths: 0
ASVspoof paths: 0
```
---
## 6. Result
Before GLOBE adaptation:
| Threshold | False fake rate |
|---|---:|
| 0.65 | 89.90% |
| 0.85 | 82.32% |
After GLOBE adaptation:
| Threshold | False fake rate |
|---|---:|
| 0.65 | 1.01% |
| 0.85 | 0.00% |
This was one of the strongest results in the project.
---
## 7. Beginner Explanation
The model was still too suspicious of some global English accents. By adding a small amount of correctly labelled GLOBE real speech, the model learned that these accents are real, not fake.
---
## 8. Summary
The GLOBE subset patch improved global-accent robustness without requiring the full dataset download.
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