# The 23 vocal-burst class groups

23 groups over 118 vocal-burst label strings, each group holding names that denote the same or a very similar sound. Seeded semantically, then checked against the measured annotator confusion using directed lift, P(top1=B | requested=A) / P(top1=B), which divides out the frequency of the two attractor labels (`deep_breath` and `exasperated_sigh` are top-1 for 29 % of all events regardless of what was requested; merging on raw confusion would book a generation failure as a hit). Drift into an attractor measures lift 1.6-2.8, a genuine one-sound-two-names pair 5-24.

## Why this exists

Many of our burst class names denote the same sound. `snicker` and `chuckle` are the same laugh; `shriek` and `scream` are the same cry under two names. Scoring only exact name agreement throws away successful generations because the annotator chose a different word.

## The trap this scheme avoids

`deep_breath` and `exasperated_sigh` are the annotator's top-1 label for **29 % of all events regardless of what was requested** — when the model cannot produce the sound asked for, it falls back on a breathy exhale. Grouping on raw confusion would book that generation failure as a hit. Groups here are checked with directed **lift**, `P(top1=B | requested=A) / P(top1=B)`, which divides the attractor's own frequency out. The two populations separate cleanly: drift into an attractor measures lift 1.6–2.8, a genuine one-sound-two-names pair measures 5–24.

## Measured effect on generation hit rate

| | mean hit rate |
|---|--:|
| exact class name | 0.302 |
| **the 23 groups** | **0.537** |
| 15 coarse families | 0.609 |
| random grouping, identical group sizes (300 draws) | 0.355 |
| **net gain over the random control** | **+0.182** |

Any grouping raises a hit rate, because a group is a bigger target. Only the last row is a result. Classes at or above a 0.15 hit rate: 28 exact, 44 grouped, 34.1 under the random control.

> The group `sob` is semantically correct and empirically a failure: its members score 0.001-0.008 and the group scores below its own random control. Grouping does not substitute for the model being able to produce the sound.

## Effect on usable training material

At a floor of 100 annotated segments in **both** corpus halves, 16 individual classes clear the bar (90,924 of 126,512 segments usable); grouped, 10 groups clear it (104,694 segments). Grouping does not create more trainable units -- it creates more material per unit. The bottleneck is the real half: `hiccup` has 2 real segments, `swallow` 0, `hiss` 3.

## Are the groups right? A test each member had to pass

Groups were seeded semantically, so each member label was then checked against the data. The test is own-group **lift** — `P(label | a member of the label's group was requested) / P(label)` — and not the raw own-group share, because the share is depressed for attractors too: `deep_breath` concentrates in its own group only 14 % of the time and still plainly belongs in `breath_calm`.

**58 of 58** testable member labels are enriched in their own group. Two were not, and both were removed on 2026-09-04:

- `sigh` — own-group lift 0.68 over 116 emissions; 2.6 % came from a sigh_pos request and none from sigh_neg. A generic fallback, spread evenly. Left unmapped. Removing it also disarms a substring collision: a substring matcher reads `Exasperated Sigh` as matching `sigh`, which would silently merge sigh_neg into sigh_pos -- the one pair these groups exist to keep apart.
- `groan` — own-group lift 1.46 over 37 emissions; 8.1 % came from a sigh_neg request, most of the rest from hum, sob and swallow. Left unmapped.

Labels whose group contains no requested class cannot be tested this way and are excluded rather than scored zero.

## The groups

| group | coarse family | members | annotated segments (real / DramaBox) | clears training floor |
|---|---|---|--:|:--:|
| `laugh_soft` | `laugh` | `chuckle`, `breathy_giggle`, `childlike_giggle`, `snicker`, `nervous_giggle`, `snorting_giggle`, `giggle`, `snickering`, `snickering_giggle`, `chuckling`, `titter` | 1,037 / 14,528 | yes |
| `laugh_loud` | `laugh` | `cackle`, `guffaw`, `laughter`, `laugh` | 42 / 3,983 | no |
| `scream` | `scream` | `scream`, `shriek`, `screech`, `screaming`, `yell`, `shout` | 199 / 4,169 | yes |
| `wail` | `scream` | `mournful_wail`, `crying` | 25 / 364 | no |
| `sigh_neg` | `sigh` | `exasperated_sigh`, `frustrated_groan`, `exhausted_groan`, `displeased_grunt`, `effort_groan` | 928 / 22,684 | yes |
| `sigh_pos` | `sigh` | `relief_sigh`, `contented_sigh`, `wistful_sigh` | 360 / 9,023 | yes |
| `gasp` | `breath` | `surprised_gasp`, `fearful_gasp`, `gasp`, `sharp_inhale`, `inhale` | 538 / 7,877 | yes |
| `breath_calm` | `breath` | `deep_breath`, `deep_breathing`, `normal_breathing`, `slow_breathing` | 557 / 19,260 | yes |
| `breath_fast` | `breath` | `panting`, `heavy_breathing`, `fast_breathing`, `pant` | 496 / 2,449 | yes |
| `hum` | `hum` | `humming`, `soft_hum`, `resonant_hum`, `low_mumble`, `whispered_mumble`, `purr`, `murmur` | 355 / 12,999 | yes |
| `grunt` | `grunt` | `affirmative_grunt`, `effort_grunt` | 182 / 2,838 | yes |
| `throat` | `throat` | `cough`, `coughing`, `clears_throat`, `ahem`, `throat_clearing`, `clearing_throat` | 67 / 3,870 | no |
| `hiccup` | `throat` | `hiccup`, `hiccups` | 2 / 2,530 | no |
| `swallow` | `mouth` | `gulps`, `swallows`, `nervous_gulp`, `drinking_noises`, `slurping_noises`, `gulp`, `swallow` | 0 / 1,080 | no |
| `sob` | `sob` | `sobs`, `quiet_sob`, `convulsive_sob`, `sob`, `trembling_whimper`, `whimper`, `weep` | 9 / 931 | no |
| `moan` | `moan` | `pain_moan`, `pleasure_moan`, `moan` | 35 / 1,332 | no |
| `sniff` | `breath` | `sniff`, `snort`, `sniffle` | 38 / 2,350 | no |
| `growl` | `growl` | `growl`, `snarl` | 23 / 1,614 | no |
| `hiss` | `hiss` | `hiss` | 3 / 2,164 | no |
| `yawn` | `yawn` | `yawn` | 190 / 4,025 | yes |
| `mouth` | `mouth` | `lip_smack`, `smacks_lips`, `smack_one_s_lips`, `tongue_click`, `clicks_tongue`, `click_one_s_tongue`, `tsk`, `kissing_sounds`, `kissing_noises`, `blowing_a_kiss`, `licking_sound`, `chewing_noises`, `sucking_noise`, `spitting`, `smack` | 16 / 995 | no |
| `whistle` | `whistle` | `soft_whistle`, `sharp_whistle`, `person_whistling_playfully`, `person_whistling_to_get_attention`, `wolf_whistle`, `whistle`, `whistling` | 54 / 44 | no |
| `misc_body` | `misc_body` | `burp`, `sneeze`, `snore`, `snoring`, `gurgling`, `shivering`, `hand_slaps`, `slap_face`, `hand_scratching_head`, `finger_snaps` | 5 / 89 | no |

## Rationale, group by group

**`laugh_soft`** — Leises bis mittleres Lachen. Gemessen: snicker->chuckle 52 %, nervous_giggle->chuckle 50 %, snorting_giggle->chuckle 33 %, childlike_giggle->chuckle 21 %.

**`laugh_loud`** — Lautes, offenes Lachen. Stärkstes wechselseitiges Paar im ganzen Satz: cackle<->guffaw, min-Lift 8,3 (guffaw->cackle 31 %, cackle->guffaw 11 %).

**`scream`** — Schrei. shriek->scream 44 % bei Lift 14,9 -- der Annotator hat schlicht einen Lieblingsnamen für denselben Laut.

**`wail`** — Klagelaut. Bewusst NICHT im Schrei: mournful_wail geht zu 29 % auf scream, aber zu ebenfalls 29 % auf yawn -- das ist keine stabile Zuordnung.

**`sigh_neg`** — Genervter/erschöpfter Seufzer und Stöhnen. Einziges Paar mit echter Rückrichtung in die Attraktor-Familie: frustrated_groan->exasperated_sigh 37 %, Rücklift 4,0.

**`sigh_pos`** — Erleichterter/zufriedener/wehmütiger Seufzer. wistful_sigh->contented_sigh 9 % und ->relief_sigh 9 %; deep_breath->relief_sigh 20 %. Das nackte `sigh` wurde am 4.9. wieder entfernt, siehe unten.

**`gasp`** — Scharfes Einatmen und Luftschnappen. fearful_gasp->surprised_gasp 35 % bei Lift 24,2 -- der zweitstärkste Beleg überhaupt.

**`breath_calm`** — Ruhiges, tiefes Atmen. ACHTUNG: deep_breath ist der größte Attraktor (14,7 % aller Events). Die Lifts hier liegen bei 1,8-1,9, also im Drift-Bereich -- die Gruppe ist semantisch begründet, nicht messtechnisch.

**`breath_fast`** — Hecheln und schweres Atmen. heavy_breathing<->panting wechselseitig, min-Lift 5,8.

**`hum`** — Summen und Gemurmel. soft_hum->humming 57 % bei Rücklift 1,6. whispered_mumble geht zu 67 % auf humming -- Lift 7,9, aber Rücklift 0,0: das ist Vokabel-Asymmetrie, kein Beleg, dass beide gleich klingen.

**`grunt`** — Kurzer Kehllaut (zustimmend oder bei Anstrengung). Beide halten ihren eigenen Namen überdurchschnittlich (45 % bzw. 30 % Selbsttreffer).

**`throat`** — Husten und Räuspern. cough<->coughing wechselseitig, min-Lift 10,8.

**`hiccup`** — Schluckauf. Reiner Singular/Plural-Split derselben Sache: hiccups->hiccup 24 %, Lift 17,6.

**`swallow`** — Schlucken. Stärkstes Paar der gesamten Matrix: gulps<->swallows, min-Lift 18,4; dazu gulps<->nervous_gulp, min-Lift 7,9.

**`sob`** — Schluchzen und Wimmern. Semantisch eine Gruppe, messtechnisch ein Totalausfall: alle Mitglieder liegen bei 0-8 % Selbsttreffer und streuen in die Attraktoren.

**`moan`** — Stöhnen. Bewusst von sigh_neg getrennt: beide halten ihre eigenen Namen (12 % bzw. 10 %) und wandern nicht ineinander.

**`sniff`** — Schniefen und Schnauben. sniff hält 27 % Selbsttreffer, snort 11 %.

**`growl`** — Knurren. Die sauberste Einzelklasse im Satz: 56 % Selbsttreffer, keine nennenswerte Wanderung.

**`hiss`** — Zischen. 57 % Selbsttreffer, kein Nachbar -- bleibt bewusst allein.

**`yawn`** — Gähnen. 29 % Selbsttreffer, wandert aber zu 34 % in exasperated_sigh; bleibt allein, weil es sonst die halbe Seufzer-Gruppe schlucken würde.

**`mouth`** — Mundgeräusche. Zusammengelegt, weil jede einzelne Klasse zu selten ist, um getrennt trainiert oder gemessen zu werden.

**`whistle`** — Pfeifen.

**`misc_body`** — Restliche Körper- und Nebengeräusche. Keine Trainingsklasse, nur damit die Abdeckung vollständig ist.

## If your scorer matches labels by substring, read this

Some scorers match labels by substring rather than equality -- `reward._same_class` in this codebase does. Under such a scorer any pair where one label contains the other counts as a match, in both directions, with no sign that it happened. Where both members sit in the same group that is simply an early partial application of this scheme. Where they sit in DIFFERENT groups it silently merges two groups.

Five bare stems were removed from the map for this reason (`sigh`, `groan`, `breathing`, `grunt`, `screaming_yawn`): `sigh` and `groan` failed the lift test outright. `breathing`, `grunt` and `screaming_yawn` are too rare to test but each is a bare stem straddling a group boundary, so a substring matcher would merge two groups through it.

The map names every one of the 82 burst labels the shipped 83-class detector can emit (`no_burst` is deliberately outside the scheme -- it names the absence of a burst). This matters beyond tidiness: an emittable label with NO group is incoherent under a substring-matching scorer, because the detection counts as a strict hit for a target it shares a stem with while contributing no group hit, so the relaxed rate falls BELOW the strict rate for that clip. Four labels -- Kissing Noises, Wolf Whistle, Person Whistling to Get Attention, Slap Face -- were unmapped until 2026-09-04 and are now in `mouth`, `whistle`, `whistle` and `misc_body`.

**One cross-group pair remains and cannot be removed**: `snort` x `snorting_giggle`. `snort` (sniff) x `snorting_giggle` (laugh_soft) cannot be removed: both are real, frequently emitted labels, not bare stems. A snort is not a snorting giggle. A substring scorer will mis-credit this pair in both directions; an equality scorer will not.

## How to use it

```python
import json
spec = json.load(open('vocal_burst_groups.json'))
g = spec['label_to_group']
def group_of(label):
    return g.get(label.strip().lower().replace(' ', '_').replace('-', '_'))
# a group-level hit: group_of(predicted) == group_of(requested)
```

group_of(label) = groups[g]['members'] membership after lowercasing, replacing spaces and hyphens with underscores. A prediction counts as a group-level hit when the predicted label and the requested label fall in the same group. Report the random-grouping control alongside: any grouping raises a hit rate because a group is a bigger target, and for this scheme 0.054 of the +0.235 raw gain is that arithmetic.

## Classifier accuracy under this scheme

Same data, same split, same head — only the encoder differs. Chance moves with the number of groups, so the ratio to chance is the only column comparable across schemes; the random control answers the separate question of whether these groups are the *right* ones.

| encoder | licence | params | test set | 17 classes | 23 groups | chance | × chance | random control | net |
|---|---|---|---|--:|--:|--:|--:|--:|--:|
| `voiceclap-large-v2` | CC BY 4.0 | 7 B + LoRA | real, balanced | 0.466 | **0.605** | 0.108 | 5.6× | 0.493 | +0.112 |
| `voiceclap-large-v2` | CC BY 4.0 | 7 B + LoRA | DramaBox, balanced | 0.574 | **0.697** | 0.106 | 6.6× | 0.595 | +0.102 |
| `voiceclap-commercial` | CC BY 4.0 | 110 M | real, balanced | 0.393 | **0.515** | 0.103 | 5.0× | 0.425 | +0.090 |
| `voiceclap-commercial` | CC BY 4.0 | 110 M | DramaBox, balanced | 0.536 | **0.628** | 0.102 | 6.2× | 0.560 | +0.068 |
| `voiceclap-small-v2` | CC BY-NC 4.0 | 110 M | real, balanced | 0.402 | **0.513** | 0.108 | 4.7× | 0.433 | +0.080 |
| `voiceclap-small-v2` | CC BY-NC 4.0 | 110 M | DramaBox, balanced | 0.551 | **0.675** | 0.103 | 6.6× | 0.574 | +0.102 |

## What this does not cover

No human has listened. The entire scheme rests on the annotator's labels.