# 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.

![benchmark](benchmark.png)

---

## 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.