docs/data_dictionary.md

# Data dictionary

Column-level schemas for every CSV in `data/` and `samples/`.

## `speakers.csv` (100 rows)

| Column | Type | Description | Valid values |
|---|---|---|---|
| `id` | text | Speaker identifier. Joins to `stimuli.reference` and to `<id>R` keys in embedding files. | F01-F50, M01-M50 |
| `name` | text | Speaker name (public figure). | celebrity name |
| `group` | int | Sociophonetic group code. | 1-5 (see below) |
| `gender` | int | Speaker gender. | 1=male, 2=female |
| `age` | int | Speaker age bracket. | 1=under 45, 2=55 or older |

### Sociophonetic group mapping

The 5 sociophonetic groups partition the 100 speakers into 20 speakers each:

| Code | Group |
|---|---|
| 1 | New York City English |
| 2 | Southern American English |
| 3 | African American English |
| 4 | Latino English |
| 5 | Asian American English |

The benchmark protocol uses `group` as a stratification variable for cross-validation folds (gender-balanced, speaker-level) and for fairness analyses.

## `stimuli.csv` (9,800 rows)

| Column | Type | Description |
|---|---|---|
| `id` | text | Stimulus identifier (matches the comparison-audio basename). Key into embedding files. |
| `stimuli_type` | int | Stimulus type, 1-6. See `stimulus_types.md`. |
| `reference` | text | Reference speaker ID. Joins to `speakers.id`. |
| `comparison` | text | Comparison speaker ID for non-Type-6 pairs (NaN for Type 6). |
| `voice_clone` | int | 1 if comparison clip is an AI voice clone, 0 otherwise. |
| `correct_answer` | int | Metadata same/different label. 1=same speaker by metadata, 0=different. |
| `scale` | int | For Type 6 morphs, interpolation level in [0, 100]. 100 for non-morph pairs. |
| `num_response` | int | Number of listener judgments on this pair. |
| `same_vote` | int | Listeners who answered "same speaker". |
| `diff_vote` | int | Listeners who answered "different speaker". |
| `correct_vote` | int | Listeners whose answer matches the metadata label. |
| `incorrect_vote` | int | Listeners whose answer disagrees with the metadata label. |
| `accuracy` | float | `correct_vote / num_response`. |
| `group` | int | Reference speaker's sociophonetic group. |
| `gender` | int | Reference speaker's gender. |
| `age` | int | Reference speaker's age bracket. |

`P(same)` is computed downstream as `same_vote / num_response`.

## `participant_responses.csv` (124,876 rows)

| Column | Type | Description |
|---|---|---|
| `user_id` | int | Pseudonymized listener identifier. Tied to no external account. |
| `stimuli_id` | text | Stimulus identifier. Joins to `stimuli.id`. |
| `stimuli_type` | int | Stimulus type, 1-6. |
| `answer` | int | Listener's binary judgment. 1=same speaker, 0=different. |
| `correct` | int | 1 if `answer` matches `correct_answer` in `stimuli.csv`, 0 otherwise. |
| `know_speaker` | int | Listener-recognition probe. 1 if listener identified the reference speaker, 0 otherwise. May be missing for early-trial responses. |
| `age` | float | Listener age band (categorical, encoded as float). |
| `gender` | float | Listener gender. |
| `first_language` | float | Listener first-language flag. 0=English first, 1=other. |
| `num_stimuli_seen` | float | Cumulative stimulus count for this listener at the time of the response. |

## `stimuli_interpol.csv` (8,100 rows)

Per-stimulus metadata for Type 6 morphs.

| Column | Type | Description |
|---|---|---|
| `id` | text | Stimulus identifier. Joins to `stimuli.id`. |
| `source` | text | Source speaker A (one endpoint of the morph trajectory). |
| `target` | text | Source speaker B (other endpoint). |
| `scale` | int | Interpolation level in [0, 100]. |

(Other columns may be present and are described in their headers; the four above are the schema-stable subset used by the analysis notebook.)

## Embedding `.npz` files

Each file in `data/embeddings/` is a key-value store:

- **Keys** (`.files` attribute): audio basenames without `.wav`. 9,900 keys total: 100 references like `M01R`, `F03R` plus 9,800 comparisons like `1_F01`, `4_M12_M15B`, `6_F03_F09_50`.
- **Values**: 1-D `np.float32` arrays of shape `(embedding_dim,)`. Dim depends on the model (see `model_table.md`).

Per-layer files (`data/embeddings/layers/`) use the same keys; values have shape `(num_layers, embedding_dim)` (mean-pooled across time per layer).

## How to pair reference and comparison

Every voice pair in VIPBench is one row of `stimuli.csv`. The pairing rule is:

| Asset | Reference clip | Comparison clip |
|---|---|---|
| CSV column | `reference` (e.g., `M01`) | `id` (e.g., `1_M01`, `4_M12_M15B`) |
| Audio file | `data/audio/reference/{reference}R.wav` | `data/audio/comparison/{id}.wav` |
| Embedding key | `{reference}R` (e.g., `M01R`) | `{id}` (e.g., `1_M01`) |

Equivalently: the reference clip's basename is the speaker ID with `R` appended; the comparison clip's basename is exactly the stimulus `id`. The 100 reference embeddings (`*R`) and 9,800 comparison embeddings (`id`) together make the 9,900 keys present in every `.npz`.

Reference example: scoring a model against `P(same)`.

```python
import numpy as np, pandas as pd
from sklearn.metrics.pairwise import cosine_similarity

stim = pd.read_csv('data/stimuli.csv')
emb  = dict(np.load('data/embeddings/ecapa_tdnn.npz'))

def cos_pair(row):
    ref = emb[f'{row.reference}R'].reshape(1, -1)
    cmp = emb[row.id].reshape(1, -1)
    return cosine_similarity(ref, cmp)[0, 0]

stim['cos']    = stim.apply(cos_pair, axis=1)
stim['p_same'] = stim['same_vote'] / stim['num_response']
print(stim[['cos', 'p_same']].corr())
```

The same pattern (with `librosa.load(...)` instead of dictionary lookup) loads the corresponding audio.