README.md

---
license: other
license_name: interview-c-data-use-agreement
extra_gated_heading: Request access to InterView-C
extra_gated_prompt: >-
  InterView-C contains synchronized gaze, face, head, body and hand tracking of
  interview participants together with their transcribed speech and their answers
  to sensitive survey questions. Access is granted under a data use agreement:
  the data may be used for non-commercial research only, and any attempt to
  re-identify participants, alone or by linking with other data, is prohibited.
  Requests are reviewed manually.
extra_gated_fields:
  Name: text
  Affiliation: text
  Intended use of the data: text
  I will use the data for non-commercial research only: checkbox
  I will not attempt to re-identify participants or link the data with other sources to do so: checkbox
  I will not redistribute the data and will delete it when my research purpose ends: checkbox
extra_gated_button_content: Agree and request access
language:
- de
pretty_name: InterView-C
size_categories:
- 1M<n<10M
task_categories:
- automatic-speech-recognition
- token-classification
tags:
- virtual-reality
- multimodal
- survey-interviews
- eye-tracking
- facial-expressions
- hand-tracking
- negation
- spoken-language
configs:
- config_name: experiment
  data_files: data/session/Experiment.parquet
- config_name: player
  data_files: data/session/Player.parquet
- config_name: word
  data_files: data/transcripts/Word.parquet
- config_name: reference_word
  data_files: data/transcripts/ReferenceWord.parquet
- config_name: interview_item
  data_files: data/instrument/InterviewItem.parquet
- config_name: question_window
  data_files: data/instrument/QuestionWindow.parquet
- config_name: survey_item
  data_files: data/questionnaire/SurveyItem.parquet
- config_name: survey_response
  data_files: data/questionnaire/SurveyResponse.parquet
- config_name: survey_answer
  data_files: data/questionnaire/SurveyAnswer.parquet
- config_name: negation
  data_files: data/negation/sentences.parquet
- config_name: negation_annotations
  data_files: data/negation/annotations.parquet
- config_name: eye
  data_files: data/tracking/Eye.parquet
- config_name: head
  data_files: data/tracking/Head.parquet
- config_name: body
  data_files: data/tracking/Body.parquet
- config_name: facial
  data_files: data/tracking/Facial.parquet
- config_name: left_hand
  data_files: data/tracking/LeftHand.parquet
- config_name: right_hand
  data_files: data/tracking/RightHand.parquet
- config_name: left_finger
  data_files: data/tracking/LeftFinger.parquet
- config_name: right_finger
  data_files: data/tracking/RightFinger.parquet
---

# InterView-C

A German multimodal corpus of **27 survey interviews conducted entirely in virtual
reality**. Interviewer and interviewee meet as avatars in a shared virtual room;
the Meta Quest Pro headsets of both record gaze, face, head, body, hand and finger
tracking alongside their speech.

The corpus has three parts:

1. **VR interaction data** — synchronized tracking of both speakers, word-timed
   automatic transcripts, the time windows in which each questionnaire item was
   read out, and the questionnaire data.
2. **Reference transcripts** — a manually post-edited verbatim transcript of all
   54 recordings (one per speaker and interview), forced-aligned to the same clock.
3. **Negation annotations** — negation cue and scope labels for 1,422 transcribed
   sentences (1,398 doubly annotated), with train/validation/test splits.

**The raw audio is not part of the release.**

## Loading

```python
from datasets import load_dataset

words = load_dataset("AnonymousKiwi/InterView-C", "reference_word", split="train")
gaze  = load_dataset("AnonymousKiwi/InterView-C", "eye", split="train")
```

Every table is a single Parquet file, so `pandas.read_parquet` / DuckDB / Polars on
the files under `data/` work as well.

## Time base

All tables share **one session clock**: `timeMs` is integer milliseconds since the
Unix epoch, `time` is the same instant as a UTC timestamp. Each headset's clock was
mapped onto a shared clock with one constant offset per session; sub-second timing
follows the audio sample clock. Tracking frames, words and item windows can
therefore be joined on time directly, e.g. the interviewee's gaze during an answer:

```python
import pandas as pd
w   = pd.read_parquet("data/transcripts/ReferenceWord.parquet")
eye = pd.read_parquet("data/tracking/Eye.parquet")
ans = w[(w.experiment == 12) & (w.role == "Interviewee") & (w.line == 40)]
g   = eye[(eye.experiment == 12) & (eye.role == "Interviewee")
          & eye.timeMs.between(ans.timeMs.min(), ans.timeMs.max())]
```

Every row carries `experiment` (interview id) and `role` (`Interviewer` /
`Interviewee`). `playerId` identifies a headset *session*: a participant who
reconnected has several sessions within one role (see `Player.playerIds`).

## Tables

| Config | File | Rows | Content |
|---|---|---:|---|
| `experiment` | `session/Experiment.parquet` | 27 | interview start/end, `interviewerId` (anonymous code 3/4/5) |
| `player` | `session/Player.parquet` | 54 | one row per speaker and interview; `playerIds` lists every headset session |
| `eye` | `tracking/Eye.parquet` | 1,350,572 | binocular gaze poses, validity, confidence |
| `head` | `tracking/Head.parquet` | 1,350,572 | 6-DoF head pose |
| `body` | `tracking/Body.parquet` | 1,350,572 | 6-DoF rig (body root) pose |
| `facial` | `tracking/Facial.parquet` | 1,350,572 | 63 face-expression weights |
| `left_hand` / `right_hand` | `tracking/{Left,Right}Hand.parquet` | 1,350,572 each | 6-DoF hand/controller anchor pose |
| `left_finger` / `right_finger` | `tracking/{Left,Right}Finger.parquet` | 1,350,572 each | hand-tracking skeleton, root/pointer pose, pinch, confidence |
| `word` | `transcripts/Word.parquet` | 53,475 | automatic word tokens (CrisperWhisper), time-aligned |
| `reference_word` | `transcripts/ReferenceWord.parquet` | 55,764 | post-edited reference word tokens, forced-aligned |
| `interview_item` | `instrument/InterviewItem.parquet` | 56 | scripted question stems and battery statements |
| `question_window` | `instrument/QuestionWindow.parquet` | 1,147 | when each item was read out, located by aligning the script to the interviewer transcript |
| `survey_item` | `questionnaire/SurveyItem.parquet` | 131 | items and value labels of the three questionnaires |
| `survey_response` | `questionnaire/SurveyResponse.parquet` | 27 | one response set per interview, avatars chosen |
| `survey_answer` | `questionnaire/SurveyAnswer.parquet` | 3,537 | answers with typed missingness |
| `negation` | `negation/sentences.parquet` | 1,422 | tokens, consolidated cues/scopes, split |
| `negation_annotations` | `negation/annotations.parquet` | 2,820 | the individual annotations (A1–A3) |

Plain-text versions of both transcript layers (one file per recording) are in
`transcripts/text/{reference,automatic}/<experiment>_<Role>.txt`.

### Tracking

Logged at a median of 18.4 Hz; `counter` is the frame index within a headset
session. Coordinate frames differ by stream:

* `Body`, `Head`, `LeftHand`, `RightHand`: Unity transforms, **world space**
  (`position` x/y/z in metres, `rotation` quaternion w/x/y/z). `Hand` is the
  hand/controller anchor.
* `Eye`, `Facial`, `LeftFinger`, `RightFinger`: **raw OVRPlugin structs** in the
  native right-handed tracking space. To get Unity's convention, flip
  position `(x, y, -z)` and rotation `(-x, -y, z, w)`. `deviceTime` (Eye, Facial)
  is the headset's own sample time, not the session clock.
* `Facial.expressionWeights` holds the 63 weights in `OVRPlugin.FaceExpression`
  order; `status.IsValid` flags valid frames.
* `Eye.eyeGazes` is `[left, right]`, each with `Confidence`, `IsValid` and `Pose`.
* `*Finger` has one row per frame. `status` is the `OVRPlugin.HandStatus` bit
  field: a hand is tracked when `status & 1` (`HandTracked`), which holds for
  38.2 % (left, 515,995) and 40.3 % (right, 544,280) of frames — the rest of the
  time the participants held controllers. Use that bit, not the presence of
  `boneRotations`: frames with only `InputStateValid` set still carry bones.
  `handConfidence` is the raw float bit pattern (`1065353216` = 1.0, high).
  `boneRotations` are the 26 OpenXR hand joints (`XR_HAND_JOINT_*` order) as
  **absolute** rotations in tracking space, not parent-relative.

### Transcripts

* `Word` is the automatic CrisperWhisper transcript; `ReferenceWord` is its manual
  verbatim post-edit (filled pauses, repetitions and false starts kept; numbers up
  to thirteen spelled out). Both are word tables with `index` (running position
  per recording), `text`, `timeMs`/`time`, `endTime`, `duration`.
* `ReferenceWord.line` groups words into the annotators' transcript lines;
  `alignScore` is the mean character probability of the CTC forced alignment
  (German wav2vec 2.0). 0.3 % of reference words could not be aligned and have no
  time. Markers for unintelligible speech (`[unknown]`) have no audio to align to
  and appear only in the plain-text reference files.

### Questionnaire

`SurveyItem.source` is `faces` (55 interview items, incl. three recording-consent
prompts), `v1309` (50 items, interviewee's experience questionnaire) or `v1261`
(26 items, interviewer's protocol). `SurveyAnswer` has `valueNum` + `valueLabel`
for coded answers, `value` for free text and `missing` for missing answers:
`filtered` (not asked, filter skip), `dont_know`, `refused`, `no_answer`,
`not_collected` (questionnaire part missing). The open main-questionnaire items
(occupation, dream occupation, …) are coded only as answered/not answered; their
content is in the transcripts.

Avatars in `SurveyResponse` are coded A–D: A blue hair, B striped pullover,
C sunglasses, D headscarf.

### Negation

Following the cue/scope formulation of *SEM 2012: `negation/sentences.parquet`
holds per sentence its `tokens`, `split` (`train`/`val`/`test`, by whole
interview: 21/3/3), speaker `role`, and the consolidated `groups` (one per negation
instance: `cueIndices`, `scopeIndices`, how the scope was decided). Cues are the
union of both annotators; disputed scope tokens were resolved with a Dawid–Skene
model fitted on the training split (`negation/aggregation_report.json`).
`cueUnanimousIndices` is the stricter cue alternative. CoNLL files for training
are under `negation/conll/<split>/`: `reference.{cue,scope}.conll`,
`baseline_cue_unanimous.cue.conll`, `baseline_scope_{union,intersection}.scope.conll`;
the `.json` next to each maps CoNLL blocks to `sentenceId`.

## De-identification

* No audio, no images, no names of participants.
* In all transcript layers, names of persons, places and institutions are
  replaced by typed placeholders `[PERSON]`, `[LOCATION]`, `[ORGANIZATION]`
  — one placeholder per original token, so word counts and timing are unchanged.
* Interviewers are identified only by an anonymous code, annotators as A1–A3.
* Head and hand motion can re-identify VR users given reference recordings of the
  same person, which is why access is restricted (below).

## Access and data use agreement

The dataset is available on request. By requesting access you agree to the
following terms:

1. The data is used for **non-commercial research** only.
2. You do **not attempt to re-identify** any participant, neither from the data
   alone nor by linking it with other data, and you report any accidental
   re-identification to the dataset maintainers without disclosing it further.
3. You do **not redistribute** the data or derived data that would allow
   reconstructing it; share access by pointing others to this page.
4. You keep the data secure and delete it once your research purpose ends.
5. Publications using the data cite the InterView-C paper.

The raw audio of both speakers is withheld and is not available on request.