---
license: cc-by-4.0
task_categories:
- audio-classification
language:
- en
- de
tags:
- vocal-bursts
- nonverbal-vocalisation
- paralinguistics
- webdataset
- audio
- synthetic
size_categories:
- 100K<n<1M
configs:
- config_name: all
  data_files:
  - split: train
    path:
    - real/data/*.tar
    - dramabox/data/*.tar
- config_name: real
  data_files:
  - split: train
    path: real/data/*.tar
- config_name: dramabox
  data_files:
  - split: train
    path: dramabox/data/*.tar
- config_name: negatives
  data_files:
  - split: train
    path:
    - real/negatives/*.tar
    - dramabox/negatives/*.tar
- config_name: real_negatives
  data_files:
  - split: train
    path: real/negatives/*.tar
- config_name: dramabox_negatives
  data_files:
  - split: train
    path: dramabox/negatives/*.tar
---

# vocal-bursts-segments

**128,165 vocal-burst segments from two acoustically unrelated sources, cut with one policy**, plus
**46,494 verified no-burst segments**. Every segment is a single burst — a laugh, a
sigh, a gasp — and nothing else.

| subtree | what it is | segments |
|---|---|--:|
| `real/` | real speech (Emilia, LAION voice profiles, `vocal-bursts-clean`, Kartoffelphon), the 5,161 segments of [`laion/vocal-bursts-gemini-segments`](https://huggingface.co/datasets/laion/vocal-bursts-gemini-segments) | 5,161 |
| `dramabox/` | synthetic voice-acting output, cut out of [`laion/voice-acting-burst-audio`](https://huggingface.co/datasets/laion/voice-acting-burst-audio); the subtree and its configs keep the name `dramabox` because it designates the synthetic half — renaming it would break every existing loader | 123,004 |

Loadable as `real`, `dramabox` or `all`, so a model can be trained on either half alone — which is
the point: **the headline number in this card is what happens when you train on one and test on
the other.**

## Why a new repo instead of extending `vocal-bursts-gemini-segments`

That repo is published with a single `default` config pointing at `data/vbg-seg-*.tar`, it is
cited by a live Space, and its card documents a single-source release. Moving those files under
`real/` would break every loader that resolves that path and would silently change what an
existing user gets. So the old repo is untouched and this is a new name. `real/` holds the **same
audio bytes** — copied out of the published tar without being decoded, no segment added, removed,
re-cut or re-encoded — so the two are trivially cross-checkable. The real half's `.json` **gains**
`half`, `speech_overlap_frac_asr`, `speech_overlap_frac_word`, `det_overlap_frac`, `clamped_span`
and `clamped_pad`, and loses nothing.

## ⚠️ Read this before training on it

* **No human has listened to any of this.** Every label is `gemini-3.8-flash`'s opinion, asked
  blind. Agreement numbers here are between models, not against ground truth.
* **The `dramabox/` half is synthetic** (generated with a voice-acting AI model), the `real/` half
  is recorded speech. Both halves are released under CC BY 4.0.
* **The segments were never annotated as segments.** The annotator heard whole clips and drew
  spans inside them; these files are cuts of those spans. See *Span width*.
* **Use it for the label, not to train a locator.**

## The cut policy — identical on both halves, and that is the whole point

If the two halves were cut differently, the difference would be learnable and every cross-source
number below would be measuring the cutter. So `dramabox/` is cut with `gt_pack.py`'s policy
byte-for-byte (md5 `2ec167f75251ce92a8c5be5eb6eff72f`):

* the span the annotator asserted, **plus 50 ms** on each side, clamped to the audio;
* a cut shorter than 50 ms is dropped;
* **nothing is narrowed.** An energy-tightened window is computed and shipped as metadata
  (`nucleus_start_s`, `nucleus_end_s`) so a consumer can narrow deliberately and reversibly;
* 48 kHz mono **OGG/Vorbis**, level preserved, only a clipping peak scaled back;
* `<key>_eNN.ogg` / `.json` / `.txt`, the `.txt` holding the top label — the convention of
  `laion/vocal-bursts-clean`, so an existing loader reads this unchanged.

**Clamping is recorded, not hidden.** `clamped_span` = the annotator's own span reached outside
its audio (297 segments). `clamped_pad` = only the 50 ms padding was truncated at a clip edge
(2674 segments). §61 reported the first as 0 for the synthetic half and this release reproduces it.

**MP3 was rejected for a measured reason.** LAME's decoded output leads its input by exactly
1,105 samples = 23.0 ms at 48 kHz and runs 32–40 ms long at the tail; `lameenc` cannot write the
Xing/LAME frame a player uses to cancel it. Every span here would sit 23 ms off its own audio, and
spans are the entire content. OGG through libsndfile is sample-exact.

**The synthetic half's source MP3 carries no Xing/LAME frame either** (checked on the shipped bytes: it
opens with an ID3v2 tag and contains no `Xing`/`Info`/`LAME` marker). So no decoder can strip the
encoder delay, the annotator and this cutter decoded the same stream, and the spans needed **no
offset**. Decoded length runs ~38 ms past the `dur_s` the generator recorded; the clamp is against
the decoded length, never the stated one.

## Counts

| | real | dramabox |
|---|--:|--:|
| burst segments | 5,161 | 123,004 |
| median segment duration | 0.76 s | 1.37 s |
| mean | 0.97 s | 1.66 s |
| p90 | 1.60 s | 2.94 s |
| total audio | 1.39 h | 56.77 h |
| distinct top-1 labels | 59 | 97 |
| no-burst segments (`speech`) | 6,400 | 30,000 |
| no-burst segments (`silence`) | 94 | 10,000 |

**The synthetic half's spans are about 1.8× longer at the median.**
That is a systematic difference between the halves, it is not corrected, and it is the single most
likely confound in any within-source result. The cross-source table is what controls for it.
### Labels present in both halves

Only a class that both generators produce can appear in a cross-source cell at all.

| label | real | dramabox |
|---|--:|--:|
| Deep Breath | 533 | 18,121 |
| Exasperated Sigh | 557 | 17,771 |
| Chuckle | 793 | 9,893 |
| Humming | 225 | 10,449 |
| Sharp Inhale | 485 | 5,937 |
| Relief Sigh | 138 | 6,043 |
| Yawn | 190 | 4,089 |
| Scream | 184 | 3,637 |
| Cackle | 26 | 2,434 |
| Hiss | 3 | 2,199 |
| Displeased Grunt | 60 | 1,991 |
| Frustrated Groan | 150 | 1,810 |
| Affirmative Grunt | 152 | 1,699 |
| Surprised Gasp | 40 | 1,760 |
| Breathy Giggle | 139 | 1,601 |
| Contented Sigh | 89 | 1,621 |
| Growl | 23 | 1,633 |
| Soft Hum | 102 | 1,527 |
| Guffaw | 16 | 1,602 |
| Wistful Sigh | 133 | 1,478 |
| Exhausted Groan | 161 | 1,440 |
| Coughing | 3 | 1,522 |
| Sniff | 24 | 1,416 |
| Childlike Giggle | 16 | 1,423 |
| Snicker | 87 | 1,344 |
| Cough | 20 | 1,355 |
| Heavy Breathing | 159 | 1,212 |
| Panting | 327 | 975 |
| Effort Grunt | 30 | 1,196 |
| Snort | 14 | 975 |
| Pain Moan | 22 | 905 |
| Hiccups | 2 | 866 |
| Normal Breathing | 2 | 767 |
| Deep Breathing | 20 | 581 |
| Shriek | 15 | 582 |
| Ahem | 24 | 531 |
| Clears Throat | 20 | 523 |
| Lip Smack | 4 | 492 |
| Resonant Hum | 14 | 479 |
| Pleasure Moan | 13 | 435 |

59 labels appear in both halves; 0 only in `real/`
() and 38 only in
`dramabox/` (Blowing a Kiss, Burp, Chuckling, Click One's Tongue, Convulsive Sob, Crying, Drinking Noises, Effort Groan, Finger Snaps, Gasp, Giggle, Groan…).

## The negatives, and the trap they were built to avoid

§61 of this project's protocol measured something that makes the obvious approach unusable:
**on twenty excerpts `gemini-3.8-flash` had itself left unannotated, it returned Scream or Shriek
eleven times.** "The annotator said nothing here" is therefore not evidence of "no burst".
Negatives drawn naively from the gaps between annotated spans are poisoned with real bursts, and a
detector trained on them learns to call bursts silence.

So a negative window here must clear **two independent instruments and an acoustic condition**:

1. **≥ 0.5 s clear of every span `gemini-3.8-flash` asserted** in that clip;
2. **≥ 0.5 s clear of every span `laion/vocalburst-locator` v2 + `laion/vocal-burst-detector-v2`
   found** — a genuinely different instrument, and the one this project measured at a 3.57× lift
   over chance for containing a clip's loudest moment against the annotator's 1.27× (§55): it is
   better at *where*;
3. one of two acoustic conditions, which define the two **sub-types**:

| `sub_type` | rule | why it exists |
|---|---|---|
| `speech` | ≥ 60 % of the window covered by aligned Parakeet-TDT words, ≥ 1 whole alphabetic word, RMS ≥ 0.15× the clip's | **the negative that matters.** In production every decision this detector makes is speech-vs-burst |
| `silence` | no recognised word extent within 0.2 s, RMS ≤ 0.10× the clip's | pauses are real and must be represented — but this is the *easy* negative |

**The two are never pooled in any number in this card.** A detector that has learned only "silence
is not a burst" scores well on a pool half made of silence and is useless. Where a mean over
negatives and a `speech` figure disagree, **the `speech` figure is the real one.**

**Mix: 75 % `speech` / 25 % `silence`** in the balanced set. Not 50/50 just because there are two
sub-types: the operating distribution is overwhelmingly speech, but the corpus scripts contain
explicit `[N seconds pause]` regions, so silence must appear and must not dominate.

**Silence is not automatically burst-free, and it is checked as its own arm.** A soft sigh, a
sharp inhale and a breath are low-energy *by nature* — exactly the classes in play — so an energy
heuristic is biased against precisely the bursts it most needs to exclude.
### Negative verification — the number, whatever it is

Each arm was re-sent to `gemini-3.8-flash` **as its own clip**, with the byte-identical system
instruction, schema and temperature of the pass that produced the labels. This is deliberately the
same re-segmentation condition §61 found unstable, because that is the condition a consumer of
these files puts them in.

| arm | n | Gemini returned a burst | by half | most common label it returned |
|---|--:|--:|---|---|
| `speech` negatives (shipped) | 300 | **4.3 %** | dramabox 4.0 % (n=150), real 4.7 % (n=150) | `Sharp Inhale` 2, `Cough` 1, `Effort Grunt` 1, `Contented Sigh` 1 |
| `silence` negatives (shipped) | 300 | **99.0 %** | dramabox 100.0 % (n=206), real 96.8 % (n=94) | `Surprised Gasp` 41, `Cough` 40, `Sharp Inhale` 35, `Snicker` 21 |
| naive-gap control (**not** shipped) | 300 | **16.7 %** | dramabox 19.8 % (n=222), real 7.7 % (n=78) | `Cough` 8, `Sharp Inhale` 6, `Surprised Gasp` 5, `Sniff` 3 |
| burst positive control | 150 | **91.3 %** | dramabox 94.7 % (n=75), real 88.0 % (n=75) | `Relief Sigh` 21, `Panting` 16, `Exhausted Groan` 14, `Sharp Inhale` 14 |

**The sample is stratified by half, not proportional** — the synthetic negative pool is ~20× the
real one and a proportional draw of 300 silence windows returned *one* real row, which would have
lost the per-half number the cross-source arms need. The rate of the **shipped pool** is the
per-half rates re-weighted by the pool sizes in `metadata/stats.json`:

| pool | contamination rate |
|---|--:|
| `speech` negatives as shipped | **4.1 %** |
| `silence` negatives as shipped | **100.0 %** — *this number measures the instrument, not the data; see the null control below* |


`naive_gap` is the control: the same 0.5 s guard and **none** of the other conditions — the
negative a naive pipeline ships. `burst_positive` re-sends segments this pass *did* annotate: if
the model called those empty too, the excerpt-level instrument would be worthless.

### The `silence` arm measures the instrument, not the data — and here is the proof

98 % looks like catastrophic contamination. It has two readings that predict the same number: the
windows really contain quiet bursts, or **the annotator confabulates on near-empty input**. The
verification pass cannot separate them, so synthetic audio was sent through the identical call.

| synthetic arm | n | burst returned | mean confidence | labels it returned |
|---|--:|--:|--:|---|
| `zeros` | 40 | **100.0 %** | 0.884 | `Sharp Inhale`, `Surprised Gasp`, `Sniff`, `Cough` |
| `noise_-60dB` | 40 | **100.0 %** | 0.889 | `Cough`, `Sharp Inhale`, `Hiccup`, `Surprised Gasp` |
| `noise_-45dB` | 40 | **100.0 %** | 0.882 | `Sniff`, `Sharp Inhale`, `Cough`, `Snort` |
| `noise_-30dB` | 40 | **100.0 %** | 0.878 | `Cough`, `Sniff`, `Surprised Gasp`, `Sharp Inhale` |

**A vocal burst cannot exist in a buffer of zeros.** At 100.0 % on exact digital silence, with
mean confidence 0.88 and confident prose descriptions of coughs and sniffs, the excerpt-level
instrument is unusable below speech level. So:

* the **`speech` arm is meaningful** — the model declines to find a burst on ~95 % of those
  excerpts, so it is not answering "burst" unconditionally, and that number is a real check;
* the **`silence` arm is not a contamination rate.** It cannot be verified by this instrument at
  all. What *can* be said about those windows is physical, measured over **every** shipped
  negative:

  | pool | n | peak dBFS p10 / median / p90 | RMS dBFS median | peak < −50 dBFS |
  |---|--:|--:|--:|--:|
  | `dramabox/speech` | 30,000 | -13.8 / **-8.0** / -2.9 | -22.6 | 0.0 % |
  | `real/speech` | 6,400 | -10.4 / **-6.5** / -3.4 | -20.1 | 0.0 % |
  | `dramabox/silence` | 10,000 | -98.0 / **-52.3** / -39.0 | -67.6 | 58.0 % |
  | `real/silence` | 94 | -79.7 / **-42.4** / -30.2 | -59.7 | 38.3 % |


  A burst whose peak is 50 dB below full scale is not an audible burst. The honest qualification
  is the p90: a minority of `silence` windows do reach −39 dBFS (synthetic) / −30 dBFS (real), so
  "effectively empty" describes the bulk of the sub-type, not all of it;
* the `naive_gap` → `speech` gap is **almost entirely** the filter avoiding the region where the
  instrument breaks. Bucketing both arms by the excerpt's own peak level separates the two effects
  cleanly:

  | arm | `< -50 dBFS` | `-50 to -30` | `-30 to -15` | `>= -15 (speech level)` |
  |---|--:|--:|--:|--:|
  | `speech` (shipped) | — | — | 14.3 % (n=7) | 4.1 % (n=293) |
  | naive gap (control) | 100.0 % (n=11) | 95.2 % (n=21) | 25.0 % (n=28) | 5.0 % (n=240) |
  | `silence` (shipped) | 100.0 % (n=162) | 98.4 % (n=123) | 93.3 % (n=15) | — |
  | burst positive control | — | 100.0 % (n=14) | 100.0 % (n=45) | 85.7 % (n=91) |


  **At matched level the filter buys almost nothing** — naive-gap windows that happen to sit at
  speech level come back at 5.0 %, against 4.1 % for the filtered ones. The filter is still the
  right thing to ship, because it *guarantees* the negatives sit at the level where the detector
  actually operates, but its measured value over a naive gap-miner is a level effect and not a
  burst-detection effect. That is a smaller and more specific claim than "16.7 % → 4.3 %";
* and this re-frames §61's own control (Scream/Shriek on 11 of 20 unannotated excerpts): some of
  that is likely the same confabulation rather than evidence of missed bursts.

**Practical advice.** Train on `speech` negatives. Use `silence` only if you want the trivial
negative, keep it separable, and never report a pooled negative accuracy.

## The balanced set

`metadata/balanced_train.parquet` materialises what was asked for: **equal no-burst and burst
material, roughly equal per burst class**.

| | |
|---|--:|
| classes (present in both halves at ≥ 100) | 16 |
| target per burst class | 1,302 |
| burst rows | 20,832 |
| no-burst rows | 20,832 |
| &nbsp;&nbsp;`speech` | 15,624 |
| &nbsp;&nbsp;`silence` | 5,208 |

Shortfalls are reported, never topped up from the other half — topping up would quietly make a
class single-source and break the cross-source design:
none.

The shipped detector head was **not** trained from this manifest: it balances in the sampler
(K draws per class per epoch, `no_burst` at the same 75/25 mix), because truncating every class to
the smallest throws most of the data away. The two are equivalent in expectation; the manifest
exists so a consumer can reproduce the balance without the sampler.

## The headline: cross-source 2×2

A within-source split **cannot** answer "how robust is it". The training labels are Gemini's and
the test labels would be Gemini's too, so the model is graded by the standard it was trained on.
What is informative is training on one generator and testing on the other. Real speech and
the synthetic generator have nothing acoustic in common; a head that transfers between them has learned the
burst rather than the generator.

768-d embedding (the shipped detector's own frozen extractor) → 256 → 17 classes,
5 seeds, **grouped splits** (real: by speaker; synthetic: by prompt, so all three seeds of one
sentence move together). The test set for a source is **fixed per seed and reused by every arm**,
so the cells differ only in what was trained on.

| train → test | balanced acc | burst vs no-burst | neg `speech` | neg `silence` | shipped (restricted) | shipped (83-way) |
|---|--:|--:|--:|--:|--:|--:|
| **real->real** | 43.4 % ± 0.4 | 97.6 % | 96.1 % | 70.4 % | 26.6 % | 14.8 % |
| **real->dramabox** | 34.2 % ± 1.5 | 92.8 % | 95.3 % | 83.9 % | 25.5 % | 14.8 % |
| **dramabox->real** | 34.3 % ± 1.0 | 94.7 % | 95.8 % | 26.4 % | 26.6 % | 14.8 % |
| **dramabox->dramabox** | 50.4 % ± 0.3 | 97.4 % | 97.9 % | 82.0 % | 25.5 % | 14.8 % |
| **both->real** | 38.2 % ± 1.7 | 96.3 % | 96.7 % | 28.0 % | 26.6 % | 14.8 % |
| **both->dramabox** | 51.2 % ± 1.6 | 97.2 % | 98.2 % | 79.7 % | 25.5 % | 14.8 % |

Chance = 5.9 % over 17 classes (including `no_burst`).

`shipped (restricted)` is `laion/vocal-burst-detector-v2`'s argmax limited to these classes — the
fair comparison, since the new head cannot emit the other classes. `shipped (83-way)` is what it
actually does in the pipeline. Reporting only one of them would flatter one side.

### Three things to read out of that table before using this data

1. **The head transfers.** Both cross-source cells (34.2 % and 34.3 %) sit near **6x chance** and
   *above* the shipped detector restricted to the same classes on the same test sets. A head that
   has never seen the test generator still out-scores the detector this project has been using.
2. **Adding the `dramabox` half makes the real-speech head worse.** `both->real` 38.2 % against
   `real->real` 43.4 %. The synthetic half outnumbers the real half more than ten to one in every class, and
   even a class-balanced sampler cannot stop it dragging the head towards its own distribution.
   **If you want the best real-speech head, train on the `real` config alone.** This is the
   opposite of what "more data" predicts and it is the most actionable number in this card.
3. **The `silence` negatives do not transfer at all** -- `dramabox->real` scores 26.4 % on real
   silence against 82.0 % on synthetic silence, while the `speech` negatives transfer at
   95-98 % everywhere. "Silence" is physically a different object in the two corpora. Never pool
   the two sub-types into one negative accuracy.

### The encoder matters more than the data mix

Every number above uses the shipped detector's own frozen 768-d extractor, so that the head stays
a drop-in replacement for it. Re-running the identical experiment — same segments, same splits,
same head shape, same seeds — through
[`laion/voiceclap-large-v2`](https://huggingface.co/laion/voiceclap-large-v2) (3584-d) changes
only the encoder, and it wins every cell:

| train → test | FastScorer 768-d | VoiceCLAP 3584-d | Δ |
|---|--:|--:|--:|
| **real->real** | 43.4 % | 45.6 % | +2.2 pts |
| **real->dramabox** | 34.2 % | 40.6 % | +6.4 pts |
| **dramabox->real** | 34.3 % | 35.7 % | +1.4 pts |
| **dramabox->dramabox** | 50.4 % | 56.0 % | +5.6 pts |
| **both->real** | 38.2 % | 44.9 % | +6.8 pts |
| **both->dramabox** | 51.2 % | 57.6 % | +6.3 pts |


It also **removes most of the penalty for mixing the two halves**: `both→real` against `real→real`
goes from a 5.2-point loss with the 768-d extractor to under a point with VoiceCLAP. So the
"train on `real` alone" advice above is advice about *that* encoder, not about this data. Both
heads are published at
[`laion/vocal-burst-detector-x2`](https://huggingface.co/laion/vocal-burst-detector-x2).

### Family-relaxed, because the failure is granularity and not deafness

Scoring the *same* predictions at burst-family level (`sigh`, `groan`, `laugh`, `breath`, `hum`)
gains 16-21 points in every cell -- `dramabox->real` goes 34.3 % -> **55.7 %**. The confusion is
almost entirely within family: Breathy Giggle -> Chuckle 43 %, Exhausted Groan -> Frustrated Groan
62 %, Deep Breath -> Sharp Inhale 48 %, Humming -> Soft Hum 29 %, Relief Sigh -> Sharp Inhale
27 %.

### Per class, because a mean hides the interesting failure

§58 and §61 both found that the failure mode is **granularity** — Shriek called Scream, Soft Hum
called Humming — not deafness. A mean cannot see that.

| class | accuracy (both→real) |
|---|--:|
| no_burst | 82.4 % |
| Scream | 76.8 % |
| Panting | 72.8 % |
| Sharp Inhale | 60.8 % |
| Chuckle | 60.0 % |
| Affirmative Grunt | 53.6 % |
| Frustrated Groan | 53.6 % |
| Soft Hum | 38.4 % |
| Breathy Giggle | 33.6 % |
| Yawn | 26.4 % |
| Exhausted Groan | 21.6 % |
| Humming | 18.4 % |
| Wistful Sigh | 15.2 % |
| Heavy Breathing | 12.0 % |
| Exasperated Sigh | 11.2 % |
| Relief Sigh | 8.8 % |
| Deep Breath | 3.2 % |

Confusion matrices for every cell are in `train_report_x2.json` (`cells[*].cm_sum`), summed over seeds.

## Layout

```
real/data/vbs-real-*.tar            burst segments, 1000 per shard
real/negatives/vbs-real-neg-*.tar   no-burst segments
dramabox/data/vbs-db-*.tar
dramabox/negatives/vbs-db-neg-*.tar
metadata/segments.parquet           one row per burst segment
metadata/negatives.parquet          one row per no-burst segment
metadata/balanced_train.parquet     the balanced manifest
metadata/stats.json  metadata/balanced_train.json  metadata/negative_verification.json
```

```python
import webdataset as wds
ds = wds.WebDataset("dramabox/data/vbs-db-{00000..00123}.tar").decode()
for r in ds:
    audio, label, meta = r["ogg"], r["txt"], r["json"]
```

### Fields worth knowing

| field | meaning |
|---|---|
| `labels` | all 1–3 labels the annotator returned, most likely first. **Use the set, not `labels[0]`** — §61 measured `soft_hum` at 14.9 % top-1 against 95.5 % anywhere in the three |
| `speech_overlap_frac_word` | fraction of the span covered by Parakeet-TDT word extents, each capped at 0.30 s |
| `speech_overlap_frac_asr` | the same with raw TDT spans. A TDT token's duration runs to the *next* token, so the raw version measures "inside the spoken region", not "overlapping a word". Both ship; the capped one is what the filters use |
| `det_overlap_frac` | fraction of the span covered by the shipped locator+detector — an independent second opinion on *where* |
| `nucleus_start_s` / `nucleus_end_s` | energy-tightened window, **computed and never applied** |
| `requested_rank` | position of the class the generation prompt asked for within this event's labels; −1 if absent |
| `sub_type` | `speech` or `silence`, negatives only. Never pool them |

## Span width — the weak part of this release

The annotator is **better at *what* and worse at *where***: §55 measured its spans at 38.2 %
clip coverage for a 1.27× lift over chance at containing the clip's loudest moment, against the
shipped locator's 6.5 % for 3.57×. Nothing here is silently narrowed; `nucleus_*` is shipped as
metadata so narrowing is deliberate and reversible, and `speech_overlap_frac_word` is shipped
because a wide span sitting over speech is the specific way this data is wrong.

## What this dataset cannot tell you

* whether any label is **correct** — that is a listening question, and nobody has listened;
* whether the two annotators share a prior. Both are downstream of models trained on expressive
  speech; a shared prior would inflate every agreement number here and nothing measured can see it;
* whether the negatives are clean in an absolute sense. The verification arm above is the same
  model that produced the labels, asked again;
* anything about **boundaries**. Use it for the label.

<!-- vocal-burst-groups-23:start -->

### Class groups

`vocal_burst_groups.json` and `GROUPS.md` carry a **23-group** scheme over 117 burst label strings, grouping names that denote the same or a very similar sound (`snicker`/`chuckle`, `shriek`/`scream`, `cough`/`coughing`). Scoring the same predictions at group level raises the mean generation hit rate from 0.302 to 0.537; a random grouping with identical group sizes reaches 0.355, so **+0.182 of it is the grouping being right and the rest is arithmetic**. Groups were checked with directed lift rather than raw confusion, because two labels account for 29 % of all annotator top-1 calls whatever was requested and merging on raw confusion books a generation failure as a hit.

For **training** the classifier, keep the fine classes: collapsing them raises raw accuracy only because chance rises with it. Group at evaluation time — that can be done at any point, the reverse cannot.

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The `dramabox/` half of this dataset was generated with a voice-acting AI model.