Épée v0.2 · Signer-Robustness Benchmark
What it shows: signer variation is the core problem in sign-language AI, and the fix is more native signers and more data, not a bigger model.
The model is trained on the four signers in this release (ALPHA, BRAVO, CHARLIE, DELTA) and tested on two native Deaf signers held entirely outside this release (no shared clips, no shared phrases), so there is no train/test leakage. All signers are pseudonymized.
Setup
| Model | small BiLSTM (1 layer, hidden 64, bidirectional, mean-pool → linear) |
| Input | keypoint sequences, hands + arms (48 pts, x/y), shoulder-normalized, resampled to 24 frames |
| Task | sign (gloss) classification on 24 glosses shared by all four training signers and both held-out test signers |
| Train | 858 gloss segments from the four release signers |
| Test | two signers held entirely out of training (≈1,900 and ≈1,450 segments) |
| Chance | 4.2% (1 / 24) · 8 seeds per configuration, mean ± std |
| Metrics | top-1 accuracy and macro-F1 (the task is imbalanced; macro-F1 is the honest headline) |
Result 1 · More signers → better on a brand-new signer
Tested on a held-out signer (Signer A), never seen in training:
| # training signers | Accuracy | Macro-F1 |
|---|---|---|
| 1 | 22% | 0.13 |
| 2 | 40% | 0.24 |
| 3 | 50% | 0.32 |
| 4 | 59% | 0.38 |
Each added signer lifts accuracy on a stranger; the 4th signer alone adds +9 points. A signer the model has seen scores 65% (ceiling); chance is 4.2%. Confirmed on a second held-out signer (Signer B): 54%.
Honest note: run-to-run std narrows from ±12% (1 signer) to ±8% (4 signers), but part of that is a counting effect (more single-signer combinations at N=1 than at N=4). We therefore lead with the accuracy trend, not the variance.
Result 2 · More data → keeps climbing, not saturated
Same held-out signer; training set grown by a clean random fraction of the four-signer pool:
| Training examples | 80 | 160 | 260 | 380 | 520 | 680 | 858 |
|---|---|---|---|---|---|---|---|
| Accuracy | 23% | 30% | 33% | 43% | 42% | 54% | 61% |
The curve rises from 23% to 61% and is still climbing at the largest size. More data directly buys more generalization to an unseen signer.
Takeaway
Trained on one Deaf signer, the recognizer gets 22% on a signer it has never seen. Trained on four, it reaches 59%, and the curve (in both signers and data) has not flattened. A bigger model does not close this gap; it is a property of the data. This is why CLERC builds the multi-signer data layer.
Caveats
- 24-gloss shared subset; the full vocabulary will differ, but the shape (more signers/data → up) should hold.
- Measures generalization across signer bodies, not across topics; cross-vocabulary generalization is a separate, harder test.
- "Not saturated" is supported by the signer curve and 7 data points, not proven asymptotically.
- Small BiLSTM by design; a bigger model does not close the 1-signer-vs-4-signer gap, which is a data property.
- The two held-out test signers come from the broader CLERC corpus and are not part of this public release.