Épée v0.3: 1,200 clips, 6 signers, 200-phrase parallel grid over 5 signers + a second register (FOXTROT), pose-guided head silhouette

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+keypoints/clerc_v03_1168.npy filter=lfs diff=lfs merge=lfs -text
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+keypoints/clerc_v03_1199.npy filter=lfs diff=lfs merge=lfs -text
+keypoints/clerc_v03_1200.npy filter=lfs diff=lfs merge=lfs -text

BENCHMARK.md

@@ -1,11 +1,15 @@
-# Épée v0.2 · Signer-Robustness Benchmark
+# Épée v0.3 · 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.
+*more native signers and more data*, not a bigger model. Re-measured on the six v0.3 signers
+with the exact v0.2 protocol, so the two releases are directly comparable.
 
-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.
+The model is trained on the six signers in this release (ALPHA through FOXTROT) and tested on
+**two native Deaf signers with no clips in this release** (Signer A and Signer B, pseudonymized,
+from the broader CLERC corpus). The held-out signers recorded under the same protocol and share
+prompts with the release signers. That is by design, not leakage: same content, brand-new body,
+which is exactly the controlled comparison a signer-generalization test needs. No clip and no
+recording is shared between train and test.
 
 ![benchmark](benchmark.png)
 
@@ -17,55 +21,75 @@ there is no train/test leakage. All signers are pseudonymized.
 |---|---|
 | 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) |
+| Task | sign (gloss) classification on the **same 24 glosses** as the v0.2 measurement (vocabulary frozen, not recomputed) |
+| Train | 1,393 gloss segments from the six release signers (858 in v0.2) |
+| Test | Signer A: 1,961 segments · Signer B: 1,453 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) |
 
+Everything pinned against v0.2: vocabulary and class order, held-out signers and their exact
+test segments, model, features, seeds, aggregation. Only the training pool changed.
+
 ---
 
 ## Result 1 · More signers → better on a brand-new signer
 
-Tested on a held-out signer (Signer A), never seen in training:
+Tested on Signer A, never seen in training. Mean over all signer combinations at each size,
+as in v0.2 (v0.2 values in grey):
 
-| # training signers | Accuracy | Macro-F1 |
-|---|---|---|
-| 1 | 22% | 0.13 |
-| 2 | 40% | 0.24 |
-| 3 | 50% | 0.32 |
-| **4** | **59%** | **0.38** |
+| # training signers | Accuracy | Macro-F1 | v0.2 accuracy |
+|---|---|---|---|
+| 1 | 29% ± 15 | 0.17 | 22% |
+| 2 | 47% ± 12 | 0.30 | 40% |
+| 3 | 57% ± 10 | 0.39 | 50% |
+| 4 | 63% ± 8 | 0.47 | 59% |
+| 5 | 65% ± 7 | 0.51 | - |
+| **6** | **69% ± 5** | **0.57** | - |
 
-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%**.
+The curve keeps rising through six signers, and the run-to-run spread narrows from ±15 to ±5
+along the way. A signer the model *has* seen scores 73% (ceiling): **the stranger gap, 43 points
+at one training signer, is down to 3 points at six.** Confirmed on Signer B: 58% (54% in v0.2).
 
-> *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.
+Every point also beats its v0.2 counterpart at the same signer count (29 vs 22, 47 vs 40, 57 vs
+50, 63 vs 59). Same protocol, so the lift is the data: 200 clips per signer instead of 150, and
+a wider phrase grid.
 
 ## 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:
+Same held-out signer; training set grown by a clean random fraction of the six-signer pool:
 
-| Training examples | 80 | 160 | 260 | 380 | 520 | 680 | 858 |
-|---|---|---|---|---|---|---|---|
-| Accuracy | 23% | 30% | 33% | 43% | 42% | 54% | **61%** |
+| Training examples | 80 | 160 | 260 | 380 | 520 | 680 | 858 | 1,044 | 1,393 |
+|---|---|---|---|---|---|---|---|---|---|
+| Accuracy | 22% | 34% | 41% | 53% | 55% | 68% | 64% | 63% | **71%** |
 
-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.
+From 22% to 71% across the pool, above the v0.2 curve at almost every budget, and still rising
+at the largest size. The dip around 858 to 1,044 is within the ±5 run-to-run spread, not a trend.
 
 ---
 
 ## 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.
+> Trained on one Deaf signer, the recognizer gets **29%** on a signer it has never seen. Trained
+> on six, it reaches **69%**, three points under the 73% it scores on signers it knows. The
+> stranger gap is closing, and it is the data doing it: more signers, more clips per signer. 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.
+- 24-gloss shared subset (frozen from v0.2 for comparability); the full vocabulary will differ,
+  but the *shape* (more signers/data → up) should hold.
+- FOXTROT covers 18 of the 24 glosses: his prompt set is a different register (declarative
+  sentences, not conversational phrases). On this conversational test signer, the five grid
+  signers alone reach 70.5%, statistically indistinguishable from the full six at 69.4%. His
+  contribution to the release is register and vocabulary breadth, not extra accuracy on this
+  particular test. Combinations that include him at small k pull the low end of the spread down,
+  which is part of why the k=1 std is wide.
+- The 5→6 rise in the table is partly composition: at k=5, the mean averages mixes where FOXTROT
+  replaces a grid signer (worst combo 58.4%). Read the trend, not any single step.
+- Measures generalization across signer **bodies**, not across topics; cross-vocabulary
+  generalization is a separate, harder test.
+- Small BiLSTM by design; a bigger model does not close the 1-signer-vs-6-signer gap, which is a
+  data property.
+- A correction to the v0.2 card's wording: the held-out signers do share prompts with the release
+  signers (same recording protocol). "No shared clips" is the true and relevant guarantee; the
+  earlier "no shared phrases" claim was inaccurate and is retired.

README.md

@@ -14,54 +14,81 @@ tags:
 - accessibility
 - deaf
 - benchmark
-pretty_name: "CLERC Épée v0.2 — Sign Language Data Layer"
+pretty_name: "CLERC Épée v0.3 - Sign Language Data Layer"
 size_categories:
-- n<1K
+- 1K<n<10K
 task_categories:
 - feature-extraction
 - translation
 - token-classification
 ---
 
-# CLERC Épée v0.2
+# CLERC Épée v0.3
 
 **The first AI-grade sign language data layer.**
 
+![clips](https://img.shields.io/badge/clips-1,200-10b981) ![duration](https://img.shields.io/badge/signed_duration-70.86_min-10b981) ![signers](https://img.shields.io/badge/Deaf_signers-6-10b981) ![phrases](https://img.shields.io/badge/parallel_grid-201_phrases_×_5_signers-0ea5e9) ![keypoints](https://img.shields.io/badge/keypoints-128_per_frame-0ea5e9) ![license](https://img.shields.io/badge/license-CC_BY--NC--SA_4.0-64748b)
+
 > **CLERC** builds the data infrastructure layer for sign language in multimodal AI.
 > 🌐 [clerc.io](https://clerc.io) · 💻 [Toolkit on GitHub](https://github.com/melouxflorian-jpg/epee-asl-toolkit) · ✉️ [florian@clerc.io](mailto:florian@clerc.io)
 > Commercial licensing and full corpus access on request.
 
-**Load this dataset in five minutes** with the open Python toolkit: [`epee-asl-toolkit`](https://github.com/melouxflorian-jpg/epee-asl-toolkit) — loaders, keypoint visualization, inter-signer variability analysis.
+The corpus is built around one idea: **the same phrase, signed by several Deaf signers, in a controlled recording protocol.** This is what that looks like, straight from the release keypoints:
+
+![One phrase, five signers](assets/hero_parallel.gif)
+
+Every clip in the parallel grid has this property. Inter-signer variation - style, rhythm, signing space, handshape choices - stops being noise you fight and becomes the thing you can measure.
+
+**v0.3 expands the signer pool from 4 to 6 and the corpus from 600 to 1,200 clips**, widens the parallel grid from 150 to 201 phrases, and adds a signer recorded on a distinct register.
+
+> CLERC builds the data layer underneath sign language AI - not a translation tool, not an accessibility app. Infrastructure.
+
+## Quick start
+
+```python
+import json, numpy as np, pandas as pd
+from pathlib import Path
+from huggingface_hub import snapshot_download
+
+ROOT = Path(snapshot_download(repo_id="CLERC-DATA/epee", repo_type="dataset"))
+meta = pd.read_csv(ROOT / "metadata.csv")
 
-A multi-signer ASL keypoint corpus designed for AI training, research benchmarking, and inter-signer variability studies. **v0.2 expands the signer pool from 3 to 4 and the corpus from 300 to 600 clips, in a fully parallel structure** — every one of 150 phrases is signed by all four Deaf signers.
+# five renderings of the same phrase, ready to compare
+grid = meta[meta.parallel == "yes"]
+phrase = grid[grid.phrase_id == 1]
+clips = {r.signer_id: np.load(ROOT / "keypoints" / f"{r.clip_id}.npy")
+         for r in phrase.itertuples()}          # each (n_frames, 128, 3)
+```
 
-> CLERC builds the data layer underneath sign language AI — not a translation tool, not an accessibility app. Infrastructure.
+Or in five minutes with the open Python toolkit: [`epee-asl-toolkit`](https://github.com/melouxflorian-jpg/epee-asl-toolkit) - loaders, keypoint visualization, inter-signer variability analysis.
 
 ---
 
 ## Dataset Summary
 
-- **600 ASL clips** — 150 unique phrases × 4 Deaf signers (fully parallel structure)
-- **Inter-signer parallel structure** — identical phrases across all four signers for direct variability analysis
-- **Multimodal keypoints** — hands, body, eyes, mouth, head silhouette (MediaPipe-extracted)
-- **Linguistically validated** — ASL gloss annotations with temporal segmentation
+- **1,200 ASL clips, 70.86 minutes of continuous signing** - 6 Deaf signers, 200 clips each
+- **Parallel grid** - 200 phrases signed by 5 of the 6 signers, for direct inter-signer variability analysis
+- **A second register** - the 6th signer (FOXTROT) contributes 200 declarative sentences from a disjoint prompt set, so the corpus is not only short conversational phrases
+- **Explicit phrase alignment** - a `phrase_id` column pairs renderings of the same phrase across signers, no id arithmetic required
+- **Multimodal keypoints** - hands, body, eyes, mouth, head silhouette (MediaPipe-extracted)
+- **Linguistically validated** - ASL gloss annotations with temporal segmentation
 
-> **This release ships extracted keypoints and annotations only — no raw video.** Source clips remain proprietary; access is reserved for commercial licensing (contact **florian@clerc.io**).
+> **This release ships extracted keypoints and annotations only - no raw video.** Source clips remain proprietary; access is reserved for commercial licensing (contact **florian@clerc.io**).
 
-This is **v0.2**, a pilot release representing a portion of the full CLERC catalog. Full corpus access available via commercial license.
+This is **v0.3**, a pilot release representing a portion of the full CLERC catalog. Full corpus access available via commercial license.
 
 ---
 
-## Benchmark ��� why multi-signer data matters
+## Benchmark - why multi-signer data matters
 
-A small BiLSTM trained on the four release signers and tested on signers held **entirely outside** the
-training set shows the core result: **one signer does not generalize to a stranger, four do.**
+A small BiLSTM trained on the six release signers and tested on signers held **entirely outside** the
+training set shows the core result: **one signer does not generalize to a stranger, six do better than four.**
 
 ![benchmark](benchmark.png)
 
-- Train on 1 → 2 → 3 → 4 signers, tested on a brand-new signer: **22% → 40% → 50% → 59%** accuracy (macro-F1 0.13 → 0.38).
-- More data keeps lifting it: **23% → 61%** as training examples grow, and the curve is not saturated.
-- Tested on two held-out signers (no leakage), 24 shared glosses, 8 seeds.
+- Train on 1 → … → 6 signers, tested on a brand-new signer: **29% → 47% → 57% → 63% → 65% → 69%** accuracy (macro-F1 0.17 → 0.57).
+- More data keeps lifting it: **22% → 71%** as training examples grow (1,393 segments in the v0.3 pool vs 858 in v0.2).
+- Tested on two signers with **no clips in this release**, same 24-gloss subset and protocol as the v0.2 measurement, 8 seeds - the two curves are directly comparable and the v0.2 one is overlaid in the figure.
 
 Full method, numbers, and honest caveats: **[BENCHMARK.md](BENCHMARK.md)**.
 
@@ -71,39 +98,41 @@ Full method, numbers, and honest caveats: **[BENCHMARK.md](BENCHMARK.md)**.
 
 | Metric | Value |
 |---|---|
-| Total clips | 600 (150 per signer) |
-| Unique phrases | 150 (fully parallel × 4 signers) |
-| Signers | 4 (ALPHA, BRAVO, CHARLIE, DELTA) |
-| Total frames | 69,504 |
-| Mean clip length | 116 frames (≈ 3.9 s @ 30 fps) |
-| Total signed duration | 38.61 min |
-| Gloss tokens | 1,708 |
-| Unique glosses | 251 |
-| Mean segments per clip | 2.85 |
+| Total clips | 1,200 (200 per signer) |
+| Signers | 6 (ALPHA, BRAVO, CHARLIE, DELTA, ECHO, FOXTROT) |
+| Parallel grid | 1,000 clips over 201 phrases (5 signers) |
+| Second register | 200 clips, 200 phrases (FOXTROT, disjoint prompt set) |
+| Unique phrases | 401 |
+| Total frames | 127,545 |
+| Mean clip length | 106 frames (≈ 3.5 s @ 30 fps) |
+| Total signed duration | 70.86 min |
+| Gloss tokens | 3,788 |
+| Unique glosses | 724 |
+| Mean segments per clip | 3.16 |
 | MediaPipe head-silhouette detection | 99.98% of frames |
-| Frame rate | 29.95 – 30.0 fps |
+| Frame rate | 29.9 – 30.1 fps |
 | Coordinate space | MediaPipe image-normalized (signer perspective) |
 
 **Top 10 glosses** (cumulative coverage of corpus):
 
 | # | Gloss | Tokens | % of corpus |
 |---|---|---|---|
-| 1 | YOU | 273 | 16.0% |
-| 2 | QUESTION | 182 | 10.7% |
-| 3 | WHERE | 50 | 2.9% |
-| 4 | WHAT | 43 | 2.5% |
-| 5 | HAVE | 41 | 2.4% |
-| 6 | WANT | 36 | 2.1% |
-| 7 | LIKE | 32 | 1.9% |
-| 8 | HOW | 25 | 1.5% |
-| 9 | YOUR | 24 | 1.4% |
-| 10 | HOW MANY | 21 | 1.2% |
+| 1 | YOU | 418 | 11.0% |
+| 2 | QUESTION | 293 | 7.7% |
+| 3 | WHAT | 83 | 2.2% |
+| 4 | HAVE | 77 | 2.0% |
+| 5 | WHERE | 68 | 1.8% |
+| 6 | LIKE | 63 | 1.7% |
+| 7 | YOUR | 58 | 1.5% |
+| 8 | I | 52 | 1.4% |
+| 9 | WANT | 52 | 1.4% |
+| 10 | HOW | 42 | 1.1% |
 
 ---
 
 ## Languages
 
-- American Sign Language (ASL) — ISO 639-3: `ase`
+- American Sign Language (ASL) - ISO 639-3: `ase`
 - Written translations in English
 
 ---
@@ -112,8 +141,8 @@ Full method, numbers, and honest caveats: **[BENCHMARK.md](BENCHMARK.md)**.
 
 ```
 epee/
-├── keypoints/           # 600 .npy arrays, shape (n_frames, 128, 3)
-├── annotations/         # 600 .json files
+├── keypoints/           # 1,200 .npy arrays, shape (n_frames, 128, 3)
+├── annotations/         # 1,200 .json files
 └── metadata.csv         # master index (1 row per clip)
 ```
 
@@ -124,11 +153,11 @@ epee/
 | 0–20 | Left hand (21 points) | MediaPipe Hands | |
 | 21–41 | Right hand (21 points) | MediaPipe Hands | |
 | 42–53 | Upper body (12 points) | MediaPipe Pose [11:23] | shoulders, elbows, wrists, finger anchors |
-| 54–63 | Lower body (10 points) | MediaPipe Pose [23:33] | hips, knees, ankles, heels, feet — spatial context, optional |
+| 54–63 | Lower body (10 points) | MediaPipe Pose [23:33] | hips, knees, ankles, heels, feet - spatial context, optional |
 | 64–91 | Eyes + mouth only (28 points) | MediaPipe Face | privacy-preserving subset |
-| 92–127 | Head silhouette (36 points) | MediaPipe FaceMesh `FACE_OVAL` | forehead, jaw, ears — outline only, no internal features |
+| 92–127 | Head silhouette (36 points) | MediaPipe FaceMesh `FACE_OVAL` | forehead, jaw, ears - outline only, no internal features |
 
-The 36 head-silhouette landmarks come from MediaPipe FaceMesh `FACE_OVAL` indices `10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288, 397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136, 172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109` (in that traversal order). The points form a closed polygon outlining the head — no internal facial features are included, so the privacy stance is preserved.
+The 36 head-silhouette landmarks come from MediaPipe FaceMesh `FACE_OVAL` indices `10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288, 397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136, 172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109` (in that traversal order). The points form a closed polygon outlining the head - no internal facial features are included, so the privacy stance is preserved.
 
 ### Coordinate space
 
@@ -150,13 +179,13 @@ Zero values `(0, 0, 0)` indicate a landmark was not detected for that frame (e.g
 
 Glosses (uppercase ASL labels) follow a few conventions worth knowing before training:
 
-**Base gloss** — `WHAT`, `YOU`, `BATHROOM`. The standard form of a sign.
+**Base gloss** - `WHAT`, `YOU`, `BATHROOM`. The standard form of a sign.
 
-**Variants — `BASE_N`** (e.g. `SIGN_2`, `WHAT_3`). Alternative ways to sign the same English concept (different handshape, location, or movement). The number `N` is an internal disambiguator, not an intensity marker. Treat `WHAT`, `WHAT_2`, `WHAT_3` as siblings sharing the same English target.
+**Variants - `BASE_N`** (e.g. `SIGN_2`, `WHAT_3`). Alternative ways to sign the same English concept (different handshape, location, or movement). The number `N` is an internal disambiguator, not an intensity marker. Treat `WHAT`, `WHAT_2`, `WHAT_3` as siblings sharing the same English target.
 
-**Directional / movement suffixes** — `POINTER_RIGHT`, `GO_LEFT`, `HOW_RIGHT_MOVE`. These mark spatial/movement components inherent to the sign and should not be collapsed with their base form.
+**Directional / movement suffixes** - `POINTER_RIGHT`, `GO_LEFT`, `HOW_RIGHT_MOVE`. These mark spatial/movement components inherent to the sign and should not be collapsed with their base form.
 
-**Phrase repetitions** — Some clips contain the target phrase signed more than once (emphasis, demonstration, self-correction). Each occurrence is a separate gloss segment. This is natural signer behavior, not a labeling error.
+**Phrase repetitions** - Some clips contain the target phrase signed more than once (emphasis, demonstration, self-correction). Each occurrence is a separate gloss segment. This is natural signer behavior, not a labeling error.
 
 **Recommended preprocessing**
 
@@ -174,12 +203,14 @@ def has_repeat(segments):
 
 ```json
 {
-  "clip_id": "clerc_v02_001",
+  "clip_id": "clerc_v03_0001",
   "signer_id": "ALPHA",
   "sign_language": "ASL",
   "text_en": "What's up?",
   "fps": 30.0,
   "n_frames": 139,
+  "phrase_id": 1,
+  "parallel": true,
   "segments": [
     { "gloss": "WHAT'S UP", "start": 0.9, "end": 1.4 },
     { "gloss": "QUESTION",  "start": 2.0, "end": 2.8 }
@@ -187,24 +218,46 @@ def has_repeat(segments):
 }
 ```
 
+`phrase_id` identifies the phrase, not the clip: two clips sharing a `phrase_id`
+are two signers' renderings of the same phrase, and their `text_en` strings are
+identical by construction. `parallel` is `true` for the 5-signer grid and
+`false` for FOXTROT's own prompt set (whose `phrase_id`s start at 1001).
+
+### metadata.csv columns
+
+| Column | Meaning |
+|---|---|
+| `clip_id` | `clerc_v03_0001` … `clerc_v03_1200` |
+| `signer_id` | pseudonym, ALPHA … FOXTROT |
+| `phrase_id` | phrase identity, shared across signers (see above) |
+| `parallel` | `yes` inside the 5-signer grid, `no` for FOXTROT |
+| `text_en` | English phrase, identical for a given `phrase_id` |
+| `fps`, `n_frames` | clip timing |
+| `oval_detection` | fraction of frames where the head silhouette was found |
+| `gloss_sequence` | ` \| `-joined glosses, in order |
+
 ---
 
 ## Signers
 
-| signer_id | Gender | Age range | Language acquisition | Clips |
-|-----------|--------|-----------|---------------------|-------|
-| ALPHA | F | 30–40 | Native Deaf signer (ASL L1) | clerc_v02_001 → 150 |
-| BRAVO | M | 30–40 | Native Deaf signer (ASL L1) | clerc_v02_151 → 300 |
-| CHARLIE | M | 30–40 | Native Deaf signer (ASL L1) | clerc_v02_301 → 450 |
-| DELTA | F | 30–40 | Native Deaf signer (ASL L1) | clerc_v02_451 → 600 |
+| signer_id | Gender | Age range | Language acquisition | Clips | Grid |
+|-----------|--------|-----------|---------------------|-------|------|
+| ALPHA | F | 30–40 | Native Deaf signer (ASL L1) | clerc_v03_0001 → 0200 | parallel |
+| BRAVO | M | 30–40 | Native Deaf signer (ASL L1) | clerc_v03_0201 → 0400 | parallel |
+| CHARLIE | M | 30–40 | Native Deaf signer (ASL L1) | clerc_v03_0401 → 0600 | parallel |
+| DELTA | F | 30–40 | Native Deaf signer (ASL L1) | clerc_v03_0601 → 0800 | parallel |
+| ECHO | F | 18–24 | Native Deaf signer (ASL L1) | clerc_v03_0801 → 1000 | parallel |
+| FOXTROT | M | 35–45 | Native Deaf signer (ASL L1) | clerc_v03_1001 → 1200 | own set |
 
-**Demographic distribution:** 2 female / 2 male, all between 30–40 years old. All native ASL signers (Deaf, ASL as first language). Signer identities are pseudonymized.
+**Demographic distribution:** 3 female / 3 male, aged 18–45. All native ASL signers (Deaf, ASL as first language). Signer identities are pseudonymized. ALPHA–DELTA are the same four signers as v0.2, under the same pseudonyms.
 
 Signers participated under written informed consent. The signing space, framing, lighting, and recording protocol were standardized across signers.
 
-**Parallel structure:** all four signers sign the same 150 phrases. The clip blocks are phrase-aligned: `clerc_v02_001`, `_151`, `_301`, `_451` are the four signers' renderings of phrase #1, and so on — enabling direct inter-signer comparison.
+**Parallel structure:** five signers (ALPHA–ECHO) cover a grid of 201 phrases. Pair renderings through the `phrase_id` column rather than by arithmetic on `clip_id`: 5 signers on 199 phrases, 4 signers on 1 phrase, 1 signers on 1 phrase. The two phrases below full coverage are a v0.2 phrase ECHO did not record, and one extra phrase recorded by ECHO alone to bring her block to 200.
+
+**FOXTROT is deliberately not parallel.** He was recorded on a different prompt set: full declarative sentences rather than the short conversational phrases and number expressions of the grid. Only one of his phrases occurs in the other signers' material, so aligning him would have meant either 61 clips instead of 200, or dropping him. He is included as a second register, and his 200 phrases are unique to him. Filter on `parallel == "no"` to isolate him, or on `parallel == "yes"` for strictly comparable inter-signer work.
 
-**Stylistic note:** Phrase repetition rates vary by signer — natural inter-signer stylistic variation, annotated as separate gloss segments. See gloss conventions for filtering.
+**Stylistic note:** Phrase repetition rates vary by signer - natural inter-signer stylistic variation, annotated as separate gloss segments. See gloss conventions for filtering.
 
 ---
 
@@ -239,7 +292,7 @@ ROOT = Path(snapshot_download(repo_id="CLERC-DATA/epee", repo_type="dataset"))
 
 metadata = pd.read_csv(ROOT / "metadata.csv")
 
-clip_id = "clerc_v02_001"
+clip_id = "clerc_v03_0001"
 with open(ROOT / "annotations" / f"{clip_id}.json") as f:
     annotation = json.load(f)
 keypoints = np.load(ROOT / "keypoints" / f"{clip_id}.npy")
@@ -250,11 +303,20 @@ face_inner  = keypoints[:, 64:92]
 head_oval   = keypoints[:, 92:128]
 ```
 
+**Comparing signers on the same phrase** - group by `phrase_id` inside the parallel grid:
+
+```python
+grid = metadata[metadata.parallel == "yes"]
+for phrase_id, group in grid.groupby("phrase_id"):
+    # one row per signer, same text_en, directly comparable
+    print(phrase_id, group.text_en.iloc[0], list(group.signer_id))
+```
+
 ---
 
 ## License
 
-**CC BY-NC-SA 4.0** — [creativecommons.org/licenses/by-nc-sa/4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)
+**CC BY-NC-SA 4.0** - [creativecommons.org/licenses/by-nc-sa/4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)
 
 **Commercial licensing:** for enterprise use, training of commercial models, or integration into commercial products, contact **florian@clerc.io**.
 
@@ -264,20 +326,46 @@ head_oval   = keypoints[:, 92:128]
 
 CLERC is Deaf-led infrastructure. This release adheres to:
 
-- **Informed consent** — all signers have provided written consent for public release under this license
-- **Privacy protection** — face landmarks restricted to non-identifying features (eyes + mouth + head outline); full biometric data excluded
-- **Community benefit** — released to advance sign language technology research; commercial revenue supports continued Deaf-led data infrastructure
-- **No surveillance use** — must not be used for individual identification, behavioral profiling, or signer surveillance
+- **Informed consent** - all signers have provided written consent for public release under this license
+- **Privacy protection** - no video, no pixels; face landmarks restricted to non-identifying features (eyes + mouth + head outline), no face mesh, no blendshapes
+- **Community benefit** - released to advance sign language technology research; commercial revenue supports continued Deaf-led data infrastructure
+- **No surveillance use** - must not be used for individual identification, behavioral profiling, or signer surveillance
+
+### Skeletons are body data
+
+Removing the video removes the face, not the body. Body proportions in keypoint
+data act as a **soft biometric**: on this release, a plain logistic regression
+separates the six signers with **94% accuracy from nine limb-ratio features
+alone** (chance is 17%), using no hand and no face landmarks at all. We measured
+this rather than assume it, and we state it rather than let "anonymized
+skeletons" imply more than it delivers.
+
+What this means for you as a user of this data:
+
+- Pseudonymization here protects **names, not bodies**. `ALPHA` is a stable
+  identity across clips and across CLERC releases, by design, because parallel
+  analysis requires it.
+- Linking these skeletons to any other recording of the same person, in this
+  dataset or outside it, is **identification**, and it is prohibited under the
+  no-surveillance term above regardless of technical feasibility.
+- If you redistribute derivatives under the share-alike term, this property
+  travels with them. Carry this note forward.
+
+We are a Deaf-led organization publishing data about Deaf bodies. Naming this
+limitation is part of the job, not an admission against interest.
 
 ---
 
 ## Limitations
 
-- **Pilot release** — 600 clips is a baseline pilot, not a production-scale corpus
-- **4 signers** — limited inter-signer diversity; full catalog includes a broader signer pool
-- **Phrase domain** — conversational/social phrases; not domain-specific (medical, legal, technical)
-- **Reduced face landmarks** — full facial grammar (brow, cheeks, head tilt) not included
-- **Gloss only** — no morphological, prosodic, or spatial annotation layers in v0.2
+- **Pilot release** - 1,200 clips is a baseline pilot, not a production-scale corpus
+- **6 signers** - limited inter-signer diversity; full catalog includes a broader signer pool
+- **One signer outside the grid** - FOXTROT's 200 clips are not phrase-matched to the others, so strictly parallel analyses run on 1,000 clips, not 1,200
+- **Uneven grid coverage** - 5 signers on 199 phrases, 4 signers on 1 phrase, 1 signers on 1 phrase inside the grid
+- **Phrase domain** - conversational phrases, number expressions, and (FOXTROT) general declarative sentences; not domain-specific (medical, legal, technical)
+- **Reduced face landmarks** - full facial grammar (brow, cheeks, head tilt) not included
+- **Gloss only** - no morphological, prosodic, or spatial annotation layers in v0.3
+- **Skeletons are a soft biometric** - signer identity is recoverable from body proportions (see Ethical Considerations); treat pseudonyms as stable identities, not as anonymity
 
 ---
 
@@ -286,21 +374,29 @@ CLERC is Deaf-led infrastructure. This release adheres to:
 | Version | Status | Content |
 |---------|--------|---------|
 | v0.1 | Superseded | 300 clips, 3 signers, gloss + timing |
-| **v0.2** | ✅ Current | 600 clips, 4 signers (ALPHA–DELTA), 150 parallel phrases, gloss + timing |
+| v0.2 | Superseded | 600 clips, 4 signers (ALPHA–DELTA), 150 parallel phrases |
+| **v0.3** | ✅ Current | 1,200 clips, 6 signers (ALPHA–FOXTROT), 201-phrase grid + a second register |
 | v1.0 | Planned 2027 | Multi-layer annotations, broader corpus |
 
+Superseded versions stay reachable as git tags on this repo (`v0.1`, `v0.2`), so
+earlier clip ids remain resolvable for anyone who cited them:
+
+```python
+snapshot_download(repo_id="CLERC-DATA/epee", repo_type="dataset", revision="v0.2")
+```
+
 ---
 
 ## How to Cite
 
 ```bibtex
-@dataset{clerc_epee_v02_2026,
+@dataset{clerc_epee_v03_2026,
   author       = {M{\'e}loux, Florian and {CLERC}},
-  title        = {{CLERC} {\'E}p{\'e}e v0.2: Sign Language Data Layer},
+  title        = {{CLERC} {\'E}p{\'e}e v0.3: Sign Language Data Layer},
   year         = {2026},
   publisher    = {Hugging Face},
-  version      = {0.2},
-  doi          = {10.5281/zenodo.20268565},
+  version      = {0.3},
+  doi          = {10.5281/zenodo.22081248},
   url          = {https://huggingface.co/datasets/CLERC-DATA/epee}
 }
 ```
@@ -309,7 +405,7 @@ CLERC is Deaf-led infrastructure. This release adheres to:
 
 ## About CLERC
 
-CLERC builds the data infrastructure that lets AI understand sign language as a first-class language — not an accessibility afterthought.
+CLERC builds the data infrastructure that lets AI understand sign language as a first-class language - not an accessibility afterthought.
 
 > Sign language is not to be translated. It is to be inscribed.
 
@@ -319,10 +415,22 @@ CLERC builds the data infrastructure that lets AI understand sign language as a
 
 ## Changelog
 
-**v0.2 — June 2026**
+**v0.3 - August 2026**
+- Signer pool 4 → 6: added ECHO and FOXTROT. Corpus 600 → 1,200 clips, 200 per signer
+- Parallel grid widened 150 → 201 phrases across 5 signers. 5 signers on 199 phrases, 4 signers on 1 phrase, 1 signers on 1 phrase
+- FOXTROT added as a second register: 200 declarative sentences on a prompt set disjoint from the grid, flagged `parallel = no`
+- New `phrase_id` and `parallel` fields in `metadata.csv` and in each annotation, so cross-signer pairing no longer depends on clip-id arithmetic
+- New `oval_detection` column reporting per-clip head-silhouette coverage
+- Clips renumbered `clerc_v03_0001`–`clerc_v03_1200`; v0.2 ids stay resolvable under tag `v0.2`
+- Head silhouette now extracted on a pose-guided head crop rather than the full frame. MediaPipe's face detector drops faces below roughly 8% of image width; on the clips where this happened the old path lost up to 21% of frames, the new one loses none. The 92 body/hand/face-subset keypoints are produced by the unchanged pipeline
+- The four v0.2 signers keep their pseudonyms and their original recordings for the 150 phrases already published
+- Signer-robustness benchmark re-run on the six v0.3 signers, same protocol, vocabulary and held-out signers as v0.2 - the two curves are directly comparable (v0.2 overlaid in the figure)
+- Added a measured statement on skeletons as a soft biometric, replacing the v0.2 card's "full biometric data excluded" wording, which overstated what removing video achieves
+
+**v0.2 - June 2026**
 - Added 4th signer (DELTA) and expanded to 600 clips
 - Restructured to 150 fully-parallel phrases × 4 signers (phrase-aligned clip blocks)
 - Same 128 multimodal keypoints/frame and gloss schema as v0.1
 
-**v0.1 — May 2026**
-- Initial public release — 300 clips, 3 signers, parallel structure
+**v0.1 - May 2026**
+- Initial public release - 300 clips, 3 signers, parallel structure

annotations/clerc_v02_001.json

@@ -1,15 +0,0 @@
-{
-  "clip_id": "clerc_v02_001",
-  "signer_id": "ALPHA",
-  "sign_language": "ASL",
-  "text_en": "What’s up?",
-  "fps": 30.0,
-  "n_frames": 111,
-  "segments": [
-    {
-      "gloss": "WHAT'S UP",
-      "start": 1.0,
-      "end": 2.1
-    }
-  ]
-}

annotations/clerc_v02_002.json

@@ -1,30 +0,0 @@
-{
-  "clip_id": "clerc_v02_002",
-  "signer_id": "ALPHA",
-  "sign_language": "ASL",
-  "text_en": "Do you see?",
-  "fps": 30.0,
-  "n_frames": 158,
-  "segments": [
-    {
-      "gloss": "D-O",
-      "start": 0.8,
-      "end": 1.1
-    },
-    {
-      "gloss": "YOU",
-      "start": 1.3,
-      "end": 1.5
-    },
-    {
-      "gloss": "SEE",
-      "start": 1.9,
-      "end": 2.3
-    },
-    {
-      "gloss": "QUESTION",
-      "start": 2.6,
-      "end": 3.2
-    }
-  ]
-}

annotations/clerc_v02_003.json

@@ -1,25 +0,0 @@
-{
-  "clip_id": "clerc_v02_003",
-  "signer_id": "ALPHA",
-  "sign_language": "ASL",
-  "text_en": "Are you tired?",
-  "fps": 30.0,
-  "n_frames": 114,
-  "segments": [
-    {
-      "gloss": "YOU",
-      "start": 0.7,
-      "end": 1.0
-    },
-    {
-      "gloss": "TIRED",
-      "start": 1.2,
-      "end": 1.9
-    },
-    {
-      "gloss": "QUESTION",
-      "start": 2.2,
-      "end": 2.7
-    }
-  ]
-}

annotations/clerc_v02_004.json

@@ -1,25 +0,0 @@
-{
-  "clip_id": "clerc_v02_004",
-  "signer_id": "ALPHA",
-  "sign_language": "ASL",
-  "text_en": "Are you hungry?",
-  "fps": 30.0,
-  "n_frames": 124,
-  "segments": [
-    {
-      "gloss": "YOU",
-      "start": 0.5,
-      "end": 0.9
-    },
-    {
-      "gloss": "HUNGRY",
-      "start": 1.1,
-      "end": 1.5
-    },
-    {
-      "gloss": "QUESTION",
-      "start": 1.8,
-      "end": 2.3
-    }
-  ]
-}

annotations/clerc_v02_005.json

@@ -1,35 +0,0 @@
-{
-  "clip_id": "clerc_v02_005",
-  "signer_id": "ALPHA",
-  "sign_language": "ASL",
-  "text_en": "Where do you live?",
-  "fps": 30.0,
-  "n_frames": 142,
-  "segments": [
-    {
-      "gloss": "WHERE",
-      "start": 0.4,
-      "end": 1.0
-    },
-    {
-      "gloss": "D-O",
-      "start": 1.3,
-      "end": 1.5
-    },
-    {
-      "gloss": "YOU",
-      "start": 1.7,
-      "end": 1.9
-    },
-    {
-      "gloss": "LIVE_2",
-      "start": 2.1,
-      "end": 2.6
-    },
-    {
-      "gloss": "QUESTION",
-      "start": 2.8,
-      "end": 3.5
-    }
-  ]
-}

annotations/clerc_v02_006.json

@@ -1,35 +0,0 @@
-{
-  "clip_id": "clerc_v02_006",
-  "signer_id": "ALPHA",
-  "sign_language": "ASL",
-  "text_en": "Do you need to help?",
-  "fps": 30.0,
-  "n_frames": 164,
-  "segments": [
-    {
-      "gloss": "D-O",
-      "start": 0.7,
-      "end": 1.0
-    },
-    {
-      "gloss": "YOU",
-      "start": 1.1,
-      "end": 1.4
-    },
-    {
-      "gloss": "NEED",
-      "start": 1.5,
-      "end": 1.9
-    },
-    {
-      "gloss": "HELP",
-      "start": 2.1,
-      "end": 3.3
-    },
-    {
-      "gloss": "QUESTION",
-      "start": 3.5,
-      "end": 4.2
-    }
-  ]
-}

annotations/clerc_v02_007.json

@@ -1,45 +0,0 @@
-{
-  "clip_id": "clerc_v02_007",
-  "signer_id": "ALPHA",
-  "sign_language": "ASL",
-  "text_en": "How old are you?",
-  "fps": 30.0,
-  "n_frames": 213,
-  "segments": [
-    {
-      "gloss": "HOW",
-      "start": 0.7,
-      "end": 1.0
-    },
-    {
-      "gloss": "OLD",
-      "start": 1.2,
-      "end": 1.5
-    },
-    {
-      "gloss": "YOU",
-      "start": 2.1,
-      "end": 2.5
-    },
-    {
-      "gloss": "QUESTION",
-      "start": 2.7,
-      "end": 3.3
-    },
-    {
-      "gloss": "OLD",
-      "start": 3.7,
-      "end": 4.6
-    },
-    {
-      "gloss": "YOU",
-      "start": 4.8,
-      "end": 5.1
-    },
-    {
-      "gloss": "QUESTION",
-      "start": 5.3,
-      "end": 5.8
-    }
-  ]
-}

annotations/clerc_v02_008.json

@@ -1,30 +0,0 @@
-{
-  "clip_id": "clerc_v02_008",
-  "signer_id": "ALPHA",
-  "sign_language": "ASL",
-  "text_en": "How do you feel?",
-  "fps": 30.0,
-  "n_frames": 139,
-  "segments": [
-    {
-      "gloss": "HOW",
-      "start": 0.7,
-      "end": 1.1
-    },
-    {
-      "gloss": "YOU",
-      "start": 1.4,
-      "end": 1.6
-    },
-    {
-      "gloss": "FEEL",
-      "start": 1.9,
-      "end": 2.7
-    },
-    {
-      "gloss": "QUESTION",
-      "start": 2.9,
-      "end": 3.4
-    }
-  ]
-}

annotations/clerc_v02_009.json

@@ -1,15 +0,0 @@
-{
-  "clip_id": "clerc_v02_009",
-  "signer_id": "ALPHA",
-  "sign_language": "ASL",
-  "text_en": "Thank you",
-  "fps": 30.0,
-  "n_frames": 118,
-  "segments": [
-    {
-      "gloss": "THANK YOU",
-      "start": 0.9,
-      "end": 2.6
-    }
-  ]
-}

annotations/clerc_v02_010.json

@@ -1,25 +0,0 @@
-{
-  "clip_id": "clerc_v02_010",
-  "signer_id": "ALPHA",
-  "sign_language": "ASL",
-  "text_en": "I do not understand",
-  "fps": 30.0,
-  "n_frames": 148,
-  "segments": [
-    {
-      "gloss": "I",
-      "start": 0.6,
-      "end": 1.0
-    },
-    {
-      "gloss": "DON'T",
-      "start": 1.2,
-      "end": 1.5
-    },
-    {
-      "gloss": "UNDERSTAND",
-      "start": 1.7,
-      "end": 3.1
-    }
-  ]
-}

annotations/clerc_v02_011.json

@@ -1,15 +0,0 @@
-{
-  "clip_id": "clerc_v02_011",
-  "signer_id": "ALPHA",
-  "sign_language": "ASL",
-  "text_en": "Hello",
-  "fps": 30.0,
-  "n_frames": 137,
-  "segments": [
-    {
-      "gloss": "HELLO",
-      "start": 1.1,
-      "end": 3.3
-    }
-  ]
-}

annotations/clerc_v02_012.json

@@ -1,40 +0,0 @@
-{
-  "clip_id": "clerc_v02_012",
-  "signer_id": "ALPHA",
-  "sign_language": "ASL",
-  "text_en": "Nice to meet you",
-  "fps": 30.0,
-  "n_frames": 173,
-  "segments": [
-    {
-      "gloss": "NICE",
-      "start": 0.5,
-      "end": 1.0
-    },
-    {
-      "gloss": "MEET",
-      "start": 1.3,
-      "end": 1.5
-    },
-    {
-      "gloss": "YOU",
-      "start": 1.8,
-      "end": 2.3
-    },
-    {
-      "gloss": "NICE",
-      "start": 2.9,
-      "end": 3.5
-    },
-    {
-      "gloss": "MEET",
-      "start": 3.7,
-      "end": 4.1
-    },
-    {
-      "gloss": "YOU",
-      "start": 4.3,
-      "end": 4.8
-    }
-  ]
-}

annotations/clerc_v02_013.json

@@ -1,30 +0,0 @@
-{
-  "clip_id": "clerc_v02_013",
-  "signer_id": "ALPHA",
-  "sign_language": "ASL",
-  "text_en": "Good morning",
-  "fps": 30.0,
-  "n_frames": 163,
-  "segments": [
-    {
-      "gloss": "GOOD",
-      "start": 0.6,
-      "end": 1.1
-    },
-    {
-      "gloss": "MORNING",
-      "start": 1.4,
-      "end": 2.3
-    },
-    {
-      "gloss": "GOOD",
-      "start": 2.8,
-      "end": 3.2
-    },
-    {
-      "gloss": "MORNING",
-      "start": 3.5,
-      "end": 4.3
-    }
-  ]
-}

annotations/clerc_v02_014.json

@@ -1,30 +0,0 @@
-{
-  "clip_id": "clerc_v02_014",
-  "signer_id": "ALPHA",
-  "sign_language": "ASL",
-  "text_en": "Good afternoon",
-  "fps": 30.0,
-  "n_frames": 161,
-  "segments": [
-    {
-      "gloss": "GOOD",
-      "start": 0.7,
-      "end": 1.1
-    },
-    {
-      "gloss": "AFTERNOON",
-      "start": 1.3,
-      "end": 2.3
-    },
-    {
-      "gloss": "GOOD",
-      "start": 2.9,
-      "end": 3.3
-    },
-    {
-      "gloss": "AFTERNOON",
-      "start": 3.6,
-      "end": 4.5
-    }
-  ]
-}

annotations/clerc_v02_015.json

@@ -1,30 +0,0 @@
-{
-  "clip_id": "clerc_v02_015",
-  "signer_id": "ALPHA",
-  "sign_language": "ASL",
-  "text_en": "Good evening",
-  "fps": 30.0,
-  "n_frames": 158,
-  "segments": [
-    {
-      "gloss": "GOOD",
-      "start": 0.4,
-      "end": 0.9
-    },
-    {
-      "gloss": "EVENING",
-      "start": 1.2,
-      "end": 2.4
-    },
-    {
-      "gloss": "GOOD",
-      "start": 2.8,
-      "end": 3.2
-    },
-    {
-      "gloss": "EVENING",
-      "start": 3.6,
-      "end": 4.4
-    }
-  ]
-}

annotations/clerc_v02_016.json

@@ -1,20 +0,0 @@
-{
-  "clip_id": "clerc_v02_016",
-  "signer_id": "ALPHA",
-  "sign_language": "ASL",
-  "text_en": "Goodbye",
-  "fps": 30.0,
-  "n_frames": 115,
-  "segments": [
-    {
-      "gloss": "GOOD BYE",
-      "start": 0.4,
-      "end": 2.0
-    },
-    {
-      "gloss": "GOOD BYE",
-      "start": 2.2,
-      "end": 3.6
-    }
-  ]
-}

annotations/clerc_v02_017.json

@@ -1,30 +0,0 @@
-{
-  "clip_id": "clerc_v02_017",
-  "signer_id": "ALPHA",
-  "sign_language": "ASL",
-  "text_en": "My name is ___",
-  "fps": 30.0,
-  "n_frames": 225,
-  "segments": [
-    {
-      "gloss": "MY",
-      "start": 0.4,
-      "end": 0.9
-    },
-    {
-      "gloss": "NAME",
-      "start": 1.2,
-      "end": 1.6
-    },
-    {
-      "gloss": "MY",
-      "start": 3.9,
-      "end": 4.4
-    },
-    {
-      "gloss": "NAME",
-      "start": 4.7,
-      "end": 5.3
-    }
-  ]
-}

annotations/clerc_v02_018.json

@@ -1,30 +0,0 @@
-{
-  "clip_id": "clerc_v02_018",
-  "signer_id": "ALPHA",
-  "sign_language": "ASL",
-  "text_en": "What is your name?",
-  "fps": 30.0,
-  "n_frames": 126,
-  "segments": [
-    {
-      "gloss": "WHAT",
-      "start": 0.4,
-      "end": 1.0
-    },
-    {
-      "gloss": "YOUR",
-      "start": 1.3,
-      "end": 1.6
-    },
-    {
-      "gloss": "NAME",
-      "start": 1.8,
-      "end": 2.4
-    },
-    {
-      "gloss": "QUESTION",
-      "start": 2.7,
-      "end": 3.3
-    }
-  ]
-}

annotations/clerc_v02_019.json

@@ -1,25 +0,0 @@
-{
-  "clip_id": "clerc_v02_019",
-  "signer_id": "ALPHA",
-  "sign_language": "ASL",
-  "text_en": "Are you hurt?",
-  "fps": 30.0,
-  "n_frames": 114,
-  "segments": [
-    {
-      "gloss": "YOU",
-      "start": 0.9,
-      "end": 1.2
-    },
-    {
-      "gloss": "HURT",
-      "start": 1.5,
-      "end": 2.7
-    },
-    {
-      "gloss": "QUESTION",
-      "start": 2.8,
-      "end": 3.3
-    }
-  ]
-}

annotations/clerc_v02_020.json

@@ -1,45 +0,0 @@
-{
-  "clip_id": "clerc_v02_020",
-  "signer_id": "ALPHA",
-  "sign_language": "ASL",
-  "text_en": "Where are you from?",
-  "fps": 30.0,
-  "n_frames": 172,
-  "segments": [
-    {
-      "gloss": "WHERE",
-      "start": 0.3,
-      "end": 0.7
-    },
-    {
-      "gloss": "YOU",
-      "start": 0.9,
-      "end": 1.2
-    },
-    {
-      "gloss": "FROM",
-      "start": 1.6,
-      "end": 2.2
-    },
-    {
-      "gloss": "WHERE",
-      "start": 2.5,
-      "end": 3.1
-    },
-    {
-      "gloss": "YOU",
-      "start": 3.3,
-      "end": 3.6
-    },
-    {
-      "gloss": "FROM",
-      "start": 3.8,
-      "end": 4.5
-    },
-    {
-      "gloss": "QUESTION",
-      "start": 4.7,
-      "end": 5.2
-    }
-  ]
-}

annotations/clerc_v02_021.json

@@ -1,40 +0,0 @@
-{
-  "clip_id": "clerc_v02_021",
-  "signer_id": "ALPHA",
-  "sign_language": "ASL",
-  "text_en": "Are you deaf?",
-  "fps": 30.0,
-  "n_frames": 149,
-  "segments": [
-    {
-      "gloss": "YOU",
-      "start": 0.3,
-      "end": 0.7
-    },
-    {
-      "gloss": "DEAF",
-      "start": 0.9,
-      "end": 1.5
-    },
-    {
-      "gloss": "QUESTION",
-      "start": 1.7,
-      "end": 2.4
-    },
-    {
-      "gloss": "YOU",
-      "start": 2.6,
-      "end": 3.0
-    },
-    {
-      "gloss": "DEAF",
-      "start": 3.2,
-      "end": 3.8
-    },
-    {
-      "gloss": "QUESTION",
-      "start": 4.0,
-      "end": 4.5
-    }
-  ]
-}

annotations/clerc_v02_022.json

@@ -1,25 +0,0 @@
-{
-  "clip_id": "clerc_v02_022",
-  "signer_id": "ALPHA",
-  "sign_language": "ASL",
-  "text_en": "Are you student?",
-  "fps": 30.0,
-  "n_frames": 122,
-  "segments": [
-    {
-      "gloss": "YOU",
-      "start": 0.3,
-      "end": 0.8
-    },
-    {
-      "gloss": "STUDENT",
-      "start": 1.2,
-      "end": 2.4
-    },
-    {
-      "gloss": "QUESTION",
-      "start": 2.7,
-      "end": 3.5
-    }
-  ]
-}

annotations/clerc_v02_023.json

@@ -1,30 +0,0 @@
-{
-  "clip_id": "clerc_v02_023",
-  "signer_id": "ALPHA",
-  "sign_language": "ASL",
-  "text_en": "Who is your teacher ?",
-  "fps": 30.0,
-  "n_frames": 144,
-  "segments": [
-    {
-      "gloss": "WHO",
-      "start": 0.3,
-      "end": 1.1
-    },
-    {
-      "gloss": "YOUR",
-      "start": 1.3,
-      "end": 1.6
-    },
-    {
-      "gloss": "TEACHER",
-      "start": 1.9,
-      "end": 3.2
-    },
-    {
-      "gloss": "QUESTION",
-      "start": 3.3,
-      "end": 4.3
-    }
-  ]
-}

annotations/clerc_v02_024.json

@@ -1,30 +0,0 @@
-{
-  "clip_id": "clerc_v02_024",
-  "signer_id": "ALPHA",
-  "sign_language": "ASL",
-  "text_en": "Do you have backpack?",
-  "fps": 30.0,
-  "n_frames": 131,
-  "segments": [
-    {
-      "gloss": "YOU",
-      "start": 0.2,
-      "end": 0.6
-    },
-    {
-      "gloss": "HAVE",
-      "start": 0.9,
-      "end": 1.2
-    },
-    {
-      "gloss": "BACKPACK",
-      "start": 1.4,
-      "end": 2.6
-    },
-    {
-      "gloss": "QUESTION",
-      "start": 2.9,
-      "end": 3.6
-    }
-  ]
-}

annotations/clerc_v02_025.json

@@ -1,30 +0,0 @@
-{
-  "clip_id": "clerc_v02_025",
-  "signer_id": "ALPHA",
-  "sign_language": "ASL",
-  "text_en": "Where is the bathroom",
-  "fps": 30.0,
-  "n_frames": 167,
-  "segments": [
-    {
-      "gloss": "WHERE",
-      "start": 0.5,
-      "end": 1.4
-    },
-    {
-      "gloss": "BATHROOM",
-      "start": 1.7,
-      "end": 2.5
-    },
-    {
-      "gloss": "WHERE",
-      "start": 2.9,
-      "end": 3.6
-    },
-    {
-      "gloss": "BATHROOM",
-      "start": 3.9,
-      "end": 4.4
-    }
-  ]
-}

annotations/clerc_v02_026.json

@@ -1,25 +0,0 @@
-{
-  "clip_id": "clerc_v02_026",
-  "signer_id": "ALPHA",
-  "sign_language": "ASL",
-  "text_en": "Are you crying?",
-  "fps": 30.0,
-  "n_frames": 112,
-  "segments": [
-    {
-      "gloss": "YOU",
-      "start": 0.3,
-      "end": 0.8
-    },
-    {
-      "gloss": "CRY",
-      "start": 1.0,
-      "end": 2.4
-    },
-    {
-      "gloss": "QUESTION",
-      "start": 2.5,
-      "end": 3.2
-    }
-  ]
-}

annotations/clerc_v02_027.json

@@ -1,30 +0,0 @@
-{
-  "clip_id": "clerc_v02_027",
-  "signer_id": "ALPHA",
-  "sign_language": "ASL",
-  "text_en": "How are you?",
-  "fps": 30.0,
-  "n_frames": 112,
-  "segments": [
-    {
-      "gloss": "HOW_RIGHT_MOVE",
-      "start": 0.7,
-      "end": 1.1
-    },
-    {
-      "gloss": "YOU",
-      "start": 1.2,
-      "end": 1.7
-    },
-    {
-      "gloss": "HOW",
-      "start": 2.2,
-      "end": 2.4
-    },
-    {
-      "gloss": "YOU",
-      "start": 2.5,
-      "end": 3.1
-    }
-  ]
-}

annotations/clerc_v02_028.json

@@ -1,25 +0,0 @@
-{
-  "clip_id": "clerc_v02_028",
-  "signer_id": "ALPHA",
-  "sign_language": "ASL",
-  "text_en": "Are you cold?",
-  "fps": 30.0,
-  "n_frames": 113,
-  "segments": [
-    {
-      "gloss": "YOU",
-      "start": 0.9,
-      "end": 1.1
-    },
-    {
-      "gloss": "COLD",
-      "start": 1.3,
-      "end": 2.5
-    },
-    {
-      "gloss": "QUESTION",
-      "start": 2.7,
-      "end": 3.3
-    }
-  ]
-}

annotations/clerc_v02_029.json

@@ -1,40 +0,0 @@
-{
-  "clip_id": "clerc_v02_029",
-  "signer_id": "ALPHA",
-  "sign_language": "ASL",
-  "text_en": "Are you finished?",
-  "fps": 30.0,
-  "n_frames": 137,
-  "segments": [
-    {
-      "gloss": "YOU",
-      "start": 0.4,
-      "end": 0.7
-    },
-    {
-      "gloss": "FINISH",
-      "start": 0.9,
-      "end": 1.4
-    },
-    {
-      "gloss": "QUESTION",
-      "start": 1.5,
-      "end": 2.4
-    },
-    {
-      "gloss": "YOU",
-      "start": 2.6,
-      "end": 2.9
-    },
-    {
-      "gloss": "FINISH",
-      "start": 3.1,
-      "end": 3.5
-    },
-    {
-      "gloss": "QUESTION",
-      "start": 3.6,
-      "end": 4.3
-    }
-  ]
-}

annotations/clerc_v02_030.json

@@ -1,50 +0,0 @@
-{
-  "clip_id": "clerc_v02_030",
-  "signer_id": "ALPHA",
-  "sign_language": "ASL",
-  "text_en": "Where is your bottle?",
-  "fps": 30.0,
-  "n_frames": 214,
-  "segments": [
-    {
-      "gloss": "WHERE",
-      "start": 0.3,
-      "end": 0.9
-    },
-    {
-      "gloss": "YOUR",
-      "start": 1.0,
-      "end": 1.3
-    },
-    {
-      "gloss": "BOTTLE",
-      "start": 1.5,
-      "end": 2.5
-    },
-    {
-      "gloss": "BOTTLE",
-      "start": 2.7,
-      "end": 3.5
-    },
-    {
-      "gloss": "WHERE",
-      "start": 3.8,
-      "end": 4.2
-    },
-    {
-      "gloss": "YOUR",
-      "start": 4.4,
-      "end": 4.6
-    },
-    {
-      "gloss": "BOTTLE",
-      "start": 5.0,
-      "end": 5.7
-    },
-    {
-      "gloss": "QUESTION",
-      "start": 5.9,
-      "end": 6.4
-    }
-  ]
-}

annotations/clerc_v02_031.json

@@ -1,30 +0,0 @@
-{
-  "clip_id": "clerc_v02_031",
-  "signer_id": "ALPHA",
-  "sign_language": "ASL",
-  "text_en": "Where is your banana?",
-  "fps": 30.0,
-  "n_frames": 152,
-  "segments": [
-    {
-      "gloss": "WHERE",
-      "start": 0.3,
-      "end": 0.7
-    },
-    {
-      "gloss": "YOUR",
-      "start": 0.9,
-      "end": 1.3
-    },
-    {
-      "gloss": "BANANA",
-      "start": 1.7,
-      "end": 3.4
-    },
-    {
-      "gloss": "QUESTION",
-      "start": 3.5,
-      "end": 4.3
-    }
-  ]
-}

annotations/clerc_v02_032.json

@@ -1,25 +0,0 @@
-{
-  "clip_id": "clerc_v02_032",
-  "signer_id": "ALPHA",
-  "sign_language": "ASL",
-  "text_en": "What time is it?",
-  "fps": 30.0,
-  "n_frames": 117,
-  "segments": [
-    {
-      "gloss": "WHAT",
-      "start": 0.2,
-      "end": 0.7
-    },
-    {
-      "gloss": "TIME",
-      "start": 1.0,
-      "end": 2.1
-    },
-    {
-      "gloss": "QUESTION",
-      "start": 2.3,
-      "end": 3.1
-    }
-  ]
-}

annotations/clerc_v02_033.json

@@ -1,35 +0,0 @@
-{
-  "clip_id": "clerc_v02_033",
-  "signer_id": "ALPHA",
-  "sign_language": "ASL",
-  "text_en": "What happened?",
-  "fps": 30.0,
-  "n_frames": 128,
-  "segments": [
-    {
-      "gloss": "DO",
-      "start": 0.4,
-      "end": 0.7
-    },
-    {
-      "gloss": "HAPPEN",
-      "start": 0.8,
-      "end": 1.2
-    },
-    {
-      "gloss": "DO",
-      "start": 1.5,
-      "end": 1.9
-    },
-    {
-      "gloss": "HAPPEN",
-      "start": 2.0,
-      "end": 2.4
-    },
-    {
-      "gloss": "QUESTION",
-      "start": 2.6,
-      "end": 3.2
-    }
-  ]
-}

annotations/clerc_v02_034.json

@@ -1,25 +0,0 @@
-{
-  "clip_id": "clerc_v02_034",
-  "signer_id": "ALPHA",
-  "sign_language": "ASL",
-  "text_en": "Are you happy?",
-  "fps": 30.0,
-  "n_frames": 90,
-  "segments": [
-    {
-      "gloss": "YOU",
-      "start": 0.4,
-      "end": 0.7
-    },
-    {
-      "gloss": "HAPPY",
-      "start": 1.0,
-      "end": 1.7
-    },
-    {
-      "gloss": "QUESTION",
-      "start": 1.8,
-      "end": 2.6
-    }
-  ]
-}

annotations/clerc_v02_035.json

@@ -1,35 +0,0 @@
-{
-  "clip_id": "clerc_v02_035",
-  "signer_id": "ALPHA",
-  "sign_language": "ASL",
-  "text_en": "Where are you go?",
-  "fps": 30.0,
-  "n_frames": 155,
-  "segments": [
-    {
-      "gloss": "WHERE",
-      "start": 0.4,
-      "end": 1.1
-    },
-    {
-      "gloss": "GO",
-      "start": 1.3,
-      "end": 1.8
-    },
-    {
-      "gloss": "WHERE",
-      "start": 2.0,
-      "end": 2.5
-    },
-    {
-      "gloss": "GO",
-      "start": 2.7,
-      "end": 3.2
-    },
-    {
-      "gloss": "QUESTION",
-      "start": 3.4,
-      "end": 4.2
-    }
-  ]
-}

annotations/clerc_v02_036.json

@@ -1,25 +0,0 @@
-{
-  "clip_id": "clerc_v02_036",
-  "signer_id": "ALPHA",
-  "sign_language": "ASL",
-  "text_en": "Why?",
-  "fps": 30.0,
-  "n_frames": 124,
-  "segments": [
-    {
-      "gloss": "WHY",
-      "start": 0.5,
-      "end": 1.3
-    },
-    {
-      "gloss": "WHY",
-      "start": 2.1,
-      "end": 3.1
-    },
-    {
-      "gloss": "QUESTION",
-      "start": 3.4,
-      "end": 4.0
-    }
-  ]
-}

annotations/clerc_v02_037.json

@@ -1,20 +0,0 @@
-{
-  "clip_id": "clerc_v02_037",
-  "signer_id": "ALPHA",
-  "sign_language": "ASL",
-  "text_en": "How?",
-  "fps": 30.0,
-  "n_frames": 97,
-  "segments": [
-    {
-      "gloss": "HOW",
-      "start": 0.1,
-      "end": 1.8
-    },
-    {
-      "gloss": "QUESTION",
-      "start": 1.9,
-      "end": 2.7
-    }
-  ]
-}

annotations/clerc_v02_038.json

@@ -1,20 +0,0 @@
-{
-  "clip_id": "clerc_v02_038",
-  "signer_id": "ALPHA",
-  "sign_language": "ASL",
-  "text_en": "When?",
-  "fps": 30.0,
-  "n_frames": 119,
-  "segments": [
-    {
-      "gloss": "WHEN",
-      "start": 0.1,
-      "end": 1.4
-    },
-    {
-      "gloss": "WHEN",
-      "start": 1.8,
-      "end": 2.5
-    }
-  ]
-}

annotations/clerc_v02_039.json

@@ -1,25 +0,0 @@
-{
-  "clip_id": "clerc_v02_039",
-  "signer_id": "ALPHA",
-  "sign_language": "ASL",
-  "text_en": "Are you alright?",
-  "fps": 30.0,
-  "n_frames": 108,
-  "segments": [
-    {
-      "gloss": "YOU",
-      "start": 0.1,
-      "end": 0.6
-    },
-    {
-      "gloss": "ALRIGHT",
-      "start": 1.0,
-      "end": 1.9
-    },
-    {
-      "gloss": "QUESTION",
-      "start": 2.1,
-      "end": 2.9
-    }
-  ]
-}

annotations/clerc_v02_040.json

@@ -1,25 +0,0 @@
-{
-  "clip_id": "clerc_v02_040",
-  "signer_id": "ALPHA",
-  "sign_language": "ASL",
-  "text_en": "See you later",
-  "fps": 30.0,
-  "n_frames": 90,
-  "segments": [
-    {
-      "gloss": "SEE",
-      "start": 0.2,
-      "end": 0.9
-    },
-    {
-      "gloss": "YOU",
-      "start": 1.0,
-      "end": 1.3
-    },
-    {
-      "gloss": "LATER",
-      "start": 1.4,
-      "end": 2.0
-    }
-  ]
-}

annotations/clerc_v02_041.json

@@ -1,20 +0,0 @@
-{
-  "clip_id": "clerc_v02_041",
-  "signer_id": "ALPHA",
-  "sign_language": "ASL",
-  "text_en": "Who?",
-  "fps": 30.0,
-  "n_frames": 84,
-  "segments": [
-    {
-      "gloss": "WHO",
-      "start": 0.0,
-      "end": 1.9
-    },
-    {
-      "gloss": "QUESTION",
-      "start": 2.0,
-      "end": 2.6
-    }
-  ]
-}

annotations/clerc_v02_042.json

@@ -1,20 +0,0 @@
-{
-  "clip_id": "clerc_v02_042",
-  "signer_id": "ALPHA",
-  "sign_language": "ASL",
-  "text_en": "Which?",
-  "fps": 30.0,
-  "n_frames": 80,
-  "segments": [
-    {
-      "gloss": "WHICH",
-      "start": 0.0,
-      "end": 1.7
-    },
-    {
-      "gloss": "QUESTION",
-      "start": 2.0,
-      "end": 2.6
-    }
-  ]
-}

annotations/clerc_v02_043.json

@@ -1,20 +0,0 @@
-{
-  "clip_id": "clerc_v02_043",
-  "signer_id": "ALPHA",
-  "sign_language": "ASL",
-  "text_en": "Where?",
-  "fps": 30.0,
-  "n_frames": 69,
-  "segments": [
-    {
-      "gloss": "WHERE",
-      "start": 0.1,
-      "end": 1.3
-    },
-    {
-      "gloss": "QUESTION",
-      "start": 1.5,
-      "end": 2.2
-    }
-  ]
-}

annotations/clerc_v02_044.json

@@ -1,25 +0,0 @@
-{
-  "clip_id": "clerc_v02_044",
-  "signer_id": "ALPHA",
-  "sign_language": "ASL",
-  "text_en": "What?",
-  "fps": 30.0,
-  "n_frames": 112,
-  "segments": [
-    {
-      "gloss": "WHAT_2",
-      "start": 0.1,
-      "end": 1.3
-    },
-    {
-      "gloss": "WHAT_2",
-      "start": 1.5,
-      "end": 2.3
-    },
-    {
-      "gloss": "QUESTION",
-      "start": 2.6,
-      "end": 3.1
-    }
-  ]
-}

annotations/clerc_v02_045.json

@@ -1,25 +0,0 @@
-{
-  "clip_id": "clerc_v02_045",
-  "signer_id": "ALPHA",
-  "sign_language": "ASL",
-  "text_en": "Any help?",
-  "fps": 30.0,
-  "n_frames": 116,
-  "segments": [
-    {
-      "gloss": "ANY",
-      "start": 0.2,
-      "end": 1.2
-    },
-    {
-      "gloss": "HELP",
-      "start": 1.4,
-      "end": 2.1
-    },
-    {
-      "gloss": "QUESTION",
-      "start": 2.2,
-      "end": 3.1
-    }
-  ]
-}

annotations/clerc_v02_046.json

@@ -1,20 +0,0 @@
-{
-  "clip_id": "clerc_v02_046",
-  "signer_id": "ALPHA",
-  "sign_language": "ASL",
-  "text_en": "No problem",
-  "fps": 30.0,
-  "n_frames": 96,
-  "segments": [
-    {
-      "gloss": "NO",
-      "start": 0.1,
-      "end": 0.8
-    },
-    {
-      "gloss": "PROBLEM",
-      "start": 1.1,
-      "end": 2.3
-    }
-  ]
-}

annotations/clerc_v02_047.json

@@ -1,25 +0,0 @@
-{
-  "clip_id": "clerc_v02_047",
-  "signer_id": "ALPHA",
-  "sign_language": "ASL",
-  "text_en": "Really?",
-  "fps": 30.0,
-  "n_frames": 124,
-  "segments": [
-    {
-      "gloss": "REALLY?",
-      "start": 0.3,
-      "end": 1.7
-    },
-    {
-      "gloss": "REALLY?",
-      "start": 1.8,
-      "end": 2.9
-    },
-    {
-      "gloss": "QUESTION",
-      "start": 3.1,
-      "end": 3.4
-    }
-  ]
-}