---
pretty_name: "Teaming-UBA: Detecting teaming from the teammate's voice"
license: cc-by-nc-sa-4.0
language:
  - es
task_categories:
  - audio-classification
tags:
  - speech
  - teaming
  - human-agent-interaction
  - rioplatense-spanish
  - personality
  - trust-in-automation
size_categories:
  - 10K<n<100K
configs:
  - config_name: metadata
    data_files:
      - split: active_priming
        path: dataset/active-priming/dataframes/metadata.csv
      - split: neutral_priming
        path: dataset/neutral-priming/dataframes/metadata.csv
  - config_name: personal_information_answers
    data_files:
      - split: active_priming
        path: dataset/active-priming/dataframes/personal_information_answers.csv
      - split: neutral_priming
        path: dataset/neutral-priming/dataframes/personal_information_answers.csv
  - config_name: Big_Five_Inventory_survey_answers
    data_files:
      - split: active_priming
        path: dataset/active-priming/dataframes/Big_Five_Inventory_survey_answers.csv
      - split: neutral_priming
        path: dataset/neutral-priming/dataframes/Big_Five_Inventory_survey_answers.csv
  - config_name: Interpersonal_Orientation_Scale_survey_answers
    data_files:
      - split: active_priming
        path: dataset/active-priming/dataframes/Interpersonal_Orientation_Scale_survey_answers.csv
      - split: neutral_priming
        path: dataset/neutral-priming/dataframes/Interpersonal_Orientation_Scale_survey_answers.csv
  - config_name: Scale_of_Trust_in_Automated_Systems_survey_answers
    data_files:
      - split: active_priming
        path: dataset/active-priming/dataframes/Scale_of_Trust_in_Automated_Systems_survey_answers.csv
      - split: neutral_priming
        path: dataset/neutral-priming/dataframes/Scale_of_Trust_in_Automated_Systems_survey_answers.csv
  - config_name: final_survey_answers
    data_files:
      - split: active_priming
        path: dataset/active-priming/dataframes/final_survey_answers.csv
      - split: neutral_priming
        path: dataset/neutral-priming/dataframes/final_survey_answers.csv
  - config_name: intermediate_survey_answers
    data_files:
      - split: active_priming
        path: dataset/active-priming/dataframes/intermediate_survey_answers.csv
      - split: neutral_priming
        path: dataset/neutral-priming/dataframes/intermediate_survey_answers.csv
  - config_name: silero_vad_aligns
    data_files:
      - split: active_priming
        path: dataset/active-priming/dataframes/silero_vad_aligns.csv
      - split: neutral_priming
        path: dataset/neutral-priming/dataframes/silero_vad_aligns.csv
  - config_name: neutral_silero_vad_aligns
    data_files:
      - split: active_priming
        path: dataset/active-priming/dataframes/neutral_silero_vad_aligns.csv
      - split: neutral_priming
        path: dataset/neutral-priming/dataframes/neutral_silero_vad_aligns.csv
---

# Teaming-UBA: Detecting teaming from the teammate's voice

<img src="teaming-uba.png" alt="Teaming-UBA" width="330">

*[Laboratorio de Inteligencia Artificial Aplicada (LIAA)](https://liaa.dc.uba.ar/)*  
*[Instituto de Ciencias de la Computación (ICC)](https://icc.fcen.uba.ar/), UBA / CONICET*  
*Buenos Aires, Argentina*

Dataset for a study on **human–agent teaming through spoken interaction**.

Subjects played a collaborative game that required speaking with a virtual agent, and each subject interacted with two agents that provided the same task-relevant information but differed in behaviour: a neutral, tool-like agent (**N agent**) and a team-building agent (**T agent**) that used simple social strategies such as empathy, politeness and positivity. The study looks at how the agent's behaviour and the subject's personality traits relate to the perception of the agent as a teammate and to task performance, and whether the agent's behaviour can be detected from the subject's speech.


## Protocol

The game, called *What-Where*, has two roles: the subject is the *follower* and the agent is the *describer*. See the paper, currently in the process of publication, for details on how the agent was operated. Each trial has two stages:

- Stage 1 (target identification / "what") — the subject asks questions to identify a target object among 30 similar objects on a board.
- Stage 2 (target localization / "where") — the subject places the identified object at a specific position on a new board, known only to the agent, by asking about its position relative to other objects or the screen.

Before playing with each agent, subjects were shown an introductory text intended to bias their expectations of that agent, which we refer to as the *priming*: the name of the agent (Iris or Ambar), a rating on three dimensions — ability, communication and cooperation — and comments from presumed previous users. Both agents delivered the same task-relevant information, but the T agent additionally used positive-politeness strategies (greetings, expressions of enthusiasm, encouraging language after a poor outcome, diminutives).

The experiment was run in two versions, which differ only in this priming:

- **active-priming**, the main version, in which the priming favoured the T agent: the rating on ability was the same for both agents, whereas the ratings on communication and cooperation were lower for the N agent, and the comments additionally described the T agent as more involved, sociable, pleasant and helpful.
- **neutral-priming**, a second version run with a smaller number of subjects, in which the priming for the T agent was equivalent to that of the N agent. It allows the contribution of the priming to be disentangled from that of the agents' behavioural differences.

Each subject played:

- 4 training trials (always with a neutral agent, not analyzed and not included in the released audio).
- 4 trials with one agent (N or T), then 4 trials with the other. Each trial had one of three difficulty levels: easy, intermediate, difficult.


## Surveys

Subjects answered the following questionnaires: the first four on a separate day, before the session in which the game was played, and the AATL ones during that session, after each trial and at the end of each block.

- **Sociodemographic questionnaire** — age, gender, education, language, accent region, hearing and color vision.
- **Big Five Inventory (BFI)** — 44 items, 1–5 Likert scale, grouped into 5 factors: openness, conscientiousness, extraversion, agreeableness, neuroticism.
- **Interpersonal Orientation Scale (IOS)** — 26 items, 1–5 Likert scale, grouped into 4 factors: emotional support, attention, positive stimulation, social comparison.
- **Scale of Trust in Automated Systems (STAS)** — 12 items, 1–7 Likert scale, single trust-in-automation score.
- **Autonomous Agent Teammate-Likeness (AATL)** — administered in two forms: a 53-item **final** questionnaire answered twice per subject, once per agent at the end of each block (7 factors: autonomy, altruism, cooperative, relationship-building, communication, synchronized, ability), and a shorter 4-factor **intermediate** questionnaire answered after each trial (altruism, communication, relationship-building, ability).

## Download

Everything is stored uncompressed, so the tables can be downloaded without the audio.

**Tables only**: the surveys, the game logs and the alignments.

```python
from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="mgauder/Teaming-UBA",
    repo_type="dataset",
    allow_patterns="dataset/*/dataframes/*.csv",
    local_dir="Teaming-UBA",
)
```

**Everything**: the same tables plus the WAV recordings.

```python
snapshot_download(
    repo_id="mgauder/Teaming-UBA",
    repo_type="dataset",
    local_dir="Teaming-UBA",
)
```

## Layout

```
dataset/
├── active-priming/
│   ├── audios/
│   │   ├── session_289/
│   │   │   ├── block_1-trial_1-stage_1.wav
│   │   │   ├── ...
│   │   │   ├── block_2-trial_4-stage_2.wav
│   │   │   ├── neutral_reading.wav
│   │   │   └── neutral_free_speech.wav
│   │   ├── session_326/
│   │   └── ...                                  (99 sessions)
│   └── dataframes/
│       ├── metadata.csv
│       ├── personal_information_answers.csv
│       ├── Big_Five_Inventory_survey_answers.csv
│       ├── Interpersonal_Orientation_Scale_survey_answers.csv
│       ├── Scale_of_Trust_in_Automated_Systems_survey_answers.csv
│       ├── final_survey_answers.csv
│       ├── intermediate_survey_answers.csv
│       ├── silero_vad_aligns.csv
│       └── neutral_silero_vad_aligns.csv
└── neutral-priming/                             (same structure, 19 sessions)
    ├── audios/
    └── dataframes/
```

If you download the audio, keep this layout: the `dataset_path` column of `silero_vad_aligns.csv` and `neutral_silero_vad_aligns.csv` is relative to `dataset/`, so the alignments resolve to the audio files only when both sit under the same root.

## The dataset

The dataset holds the two versions of the experiment described above, one per directory:

- [`active-priming/`](dataset/active-priming/) — 99 sessions released (61 female, 37 male, 1 other; mean age 32.1, SD 10.2).
- [`neutral-priming/`](dataset/neutral-priming/) — 19 sessions released; 18 of them answered the questionnaires administered on the previous day (14 female, 3 male, 1 other; mean age 36.1, SD 8.9). 

Both versions share the same structure. Every session collected is released.

### `audios/`

`audios/session_<session_id>/` contains, per subject:

- `block_<1-2>-trial_<1-4>-stage_<1-2>.wav` — one recording per stage of each of the 8 trials (4 with each agent). `block_<1-2>` is the chronological order in which the subject played that agent (1: first, 2: second); use `metadata.csv` (`block_order`, `agent_condition`) to know which agent it corresponds to for a given subject.
- `neutral_reading.wav` — reading of a phonetically balanced text.
- `neutral_free_speech.wav` — description of a picture (at least 30 seconds).

Recordings are WAV PCM 16-bit, mono or stereo, at 16-48 kHz: sessions were remote and each subject used their own microphone and headset. Every session has its 16 stage recordings; the two neutral recordings are present for all sessions (except session number 357).


### `dataframes/`

All files are UTF-8 CSV with a header row, comma-separated, and no index column.

```python
import pandas as pd

metadata = pd.read_csv("dataset/active-priming/dataframes/metadata.csv")
subjects = pd.read_csv(
    "dataset/active-priming/dataframes/personal_information_answers.csv",
    parse_dates=["dob"],
)
```

#### `metadata.csv`
One row per trial.

<table>
<thead><tr><th>Column</th><th>Type</th><th>Description</th></tr></thead>
<tbody>
<tr><td>session_id</td><td>Integer</td><td>Subject/session identification. Each subject solves one session.</td></tr>
<tr><td>block_order</td><td>Integer (1-2)</td><td>Whether this trial belongs to the subject's first or second agent block.</td></tr>
<tr><td>block_type</td><td>Char (A/B)</td><td>Which of the two mirrored board sets was used in this block.</td></tr>
<tr><td>trial_order</td><td>Integer (1-4)</td><td>Order of the trial within its block.</td></tr>
<tr><td>trial_difficulty</td><td>String (easy / intermediate / difficult)</td><td>Difficulty level of the trial.</td></tr>
<tr><td>agent_condition</td><td>String (N agent / T agent)</td><td>Agent type for this trial.</td></tr>
<tr><td>agent_order</td><td>String (N-FIRST / T-FIRST)</td><td>Whether this subject played the N agent or the T agent first.</td></tr>
<tr><td>session_block_id</td><td>Integer</td><td>Identifier of the block this trial belongs to (one per subject × agent block; matches `session_block_id` in `final_survey_answers.csv`).</td></tr>
<tr><td>session_trial_id</td><td>Integer</td><td>Identifier of this trial; one per row (matches `session_trial_id` in `intermediate_survey_answers.csv`).</td></tr>
<tr><td>first_step_attempts</td><td>Integer</td><td>Number of attempts in Stage 1 before the subject selected the correct target object.</td></tr>
<tr><td>overlap_score</td><td>Float</td><td>Stage 2 score: overlap score between the position selected by the subject and the actual target position.</td></tr>
<tr><td>distance_score</td><td>Float</td><td>Stage 2 score: distance between the position selected by the subject and the actual target position.</td></tr>
<tr><td>string_score</td><td>String (bad / fair / good / excellent)</td><td>Stage 2 categorical performance label shown to the subject, based on `overlap_score`.</td></tr>
<tr><td>what_discontinuous_task, where_discontinuous_task</td><td>Bool</td><td>Set if Stage 1 / Stage 2 of this trial was interrupted (e.g. by a connection loss) and resumed.</td></tr>
<tr><td>agent_what_answers_count, agent_where_answers_count</td><td>Float</td><td>Number of agent responses during Stage 1 / Stage 2 of the trial.</td></tr>
</tbody>
</table>


#### `personal_information_answers.csv`
One row per subject.

<table>
<thead><tr><th>Column</th><th>Type</th><th>Description</th></tr></thead>
<tbody>
<tr><td>session_id</td><td>Integer</td><td>Subject/session identification.</td></tr>
<tr><td>dob</td><td>Date</td><td>Date of birth.</td></tr>
<tr><td>age</td><td>Float</td><td>Age at the time of the session.</td></tr>
<tr><td>gender</td><td>String (male / female / other)</td><td>Self-reported gender.</td></tr>
<tr><td>accent</td><td>String</td><td>Self-reported accent (free text).</td></tr>
<tr><td>first_language</td><td>String</td><td>Self-reported first language (free text).</td></tr>
<tr><td>born, address</td><td>String</td><td>Place of birth and current residence (free text).</td></tr>
<tr><td>address_time</td><td>String</td><td>Time living at the current address, free text as typed by the subject: mostly a number of years, but some answers are written out in Spanish (e.g. <code>6 meses</code>, <code>Toda la vida</code>).</td></tr>
<tr><td>education</td><td>String</td><td>Self-reported highest education level, normalized to: secondary_incomplete, secondary_complete, tertiary_incomplete, tertiary_complete, university_incomplete, university_complete, masters_incomplete, masters_complete, doctorate_incomplete, doctorate_complete, unknown. </td></tr>
<tr><td>hearing</td><td>Bool</td><td>Self-reported hearing impairment.</td></tr>
<tr><td>color_blindness</td><td>Bool</td><td>Self-reported color blindness.</td></tr>
</tbody>
</table>

#### `Big_Five_Inventory_survey_answers.csv`, `Interpersonal_Orientation_Scale_survey_answers.csv`, `Scale_of_Trust_in_Automated_Systems_survey_answers.csv`
One row per item answered. Same schema for the three files.

<table>
<thead><tr><th>Column</th><th>Type</th><th>Description</th></tr></thead>
<tbody>
<tr><td>session_id</td><td>Integer</td><td>Subject/session identification.</td></tr>
<tr><td>name</td><td>String</td><td>Name of the survey.</td></tr>
<tr><td>text, text_english</td><td>String</td><td>Item text, in Spanish (as shown to the subject) and English.</td></tr>
<tr><td>response</td><td>Integer</td><td>Subject's answer: 1-5 Likert scale for BFI and IOS; 1-7 for STAS.</td></tr>
<tr><td>dimension</td><td>String</td><td>Subscale the item belongs to (e.g. BFI: openness/conscientiousness/extraversion/agreeableness/neuroticism; IOS: emotional_support/attention/positive_stimulation/social_comparison; STAS: trust_in_automation).</td></tr>
<tr><td>orientation</td><td>String (direct / inverted)</td><td>Whether the item is reverse-coded when computing the dimension score.</td></tr>
</tbody>
</table>

#### `final_survey_answers.csv`, `intermediate_survey_answers.csv`
AATL questionnaire answers: `final_survey_answers.csv` holds the 53-item questionnaire answered at the end of each block, and `intermediate_survey_answers.csv` the shorter 4-factor questionnaire answered after each trial.

<table>
<thead><tr><th>Column</th><th>Type</th><th>Description</th></tr></thead>
<tbody>
<tr><td>session_id</td><td>Integer</td><td>Subject/session identification.</td></tr>
<tr><td>question_id</td><td>Integer</td><td>Item identification.</td></tr>
<tr><td>session_block_id</td><td>Integer (final only)</td><td>Block the answer refers to; matches `session_block_id` in `metadata.csv`.</td></tr>
<tr><td>session_trial_id</td><td>Integer (intermediate only)</td><td>Trial the answer refers to; matches `session_trial_id` in `metadata.csv`.</td></tr>
<tr><td>name</td><td>String</td><td>final or intermediate.</td></tr>
<tr><td>text, text_english</td><td>String</td><td>Item text, in Spanish and English (`[Agent Name]` refers to the agent being evaluated).</td></tr>
<tr><td>response</td><td>Integer (1-5)</td><td>Subject's answer, 1-5 Likert scale.</td></tr>
<tr><td>dimension</td><td>String</td><td>AATL subscale: autonomy, altruism, cooperative, relationship-building, communication, synchronized, ability (final); altruism, communication, relationship-building, ability (intermediate).</td></tr>
<tr><td>orientation</td><td>String (direct / inverted)</td><td>Whether the item is reverse-coded when computing the dimension score.</td></tr>
</tbody>
</table>

#### `neutral_silero_vad_aligns.csv`
Voice-activity alignment (Silero VAD) for the neutral speech recordings (`neutral_reading.wav`, `neutral_free_speech.wav`).

<table>
<thead><tr><th>Column</th><th>Type</th><th>Description</th></tr></thead>
<tbody>
<tr><td>session_id</td><td>Integer</td><td>Subject/session identification.</td></tr>
<tr><td>task_type</td><td>String (neutral_reading / neutral_free_speech)</td><td>Which of the two neutral recordings this segment belongs to.</td></tr>
<tr><td>start, end</td><td>Float (seconds)</td><td>Segment boundaries within the recording.</td></tr>
<tr><td>dataset_path</td><td>String</td><td>Path to the corresponding audio file, relative to <code>dataset/</code> (e.g. <code>active-priming/audios/session_289/neutral_free_speech.wav</code>).</td></tr>
</tbody>
</table>

#### `silero_vad_aligns.csv`
Voice-activity alignment (Silero VAD) for the game recordings (`block_<1-2>-trial_<1-4>-stage_<1-2>.wav`).

<table>
<thead><tr><th>Column</th><th>Type</th><th>Description</th></tr></thead>
<tbody>
<tr><td>start, end</td><td>Float (seconds)</td><td>Segment boundaries within the recording.</td></tr>
<tr><td>dataset_path</td><td>String</td><td>Path to the corresponding stage recording, relative to <code>dataset/</code>, of the form <code>active-priming/audios/session_&lt;session_id&gt;/block_&lt;block_order&gt;-trial_&lt;trial_order&gt;-stage_&lt;stage&gt;.wav</code>.</td></tr>
</tbody>
</table>


## Dataset collection

- **Inclusion criteria**: age 18-65, Rioplatense Spanish accent, normal or corrected-to-normal visual acuity and color vision.
- **Recruitment**: laboratory's social media, student mailing lists, posters on campus.
- **Ethics**: approved by the Institutional Review Board of Hospital General de Agudos J. M. Ramos Mejía (IRB No. 00001942; OHRP FWA 00001767; protocol registration code 6215; approval date November 30, 2021), under an exemption per 45 CFR §46.104. Subjects gave prospective consent after a general description of the study; full protocol details were disclosed by email after the experiment.
- Sessions were remote, using the subject's own computer, microphone and headset.
- Some sessions were interrupted (e.g. connection issues) and resumed later; these are included in the dataset (flagged via `what_discontinuous_task`/`where_discontinuous_task` in `metadata.csv`) but excluded from the analyses.

## Publication

The study associated with this dataset is currently in the process of publication. A citation will be added here once it is out.