README.md

---
license: cc-by-nc-4.0
task_categories:
  - audio-classification
  - question-answering
language:
  - en
tags:
  - speech
  - speaker-attribution
  - multi-party
  - conversational-audio
  - benchmark
  - training-data
  - hard-negatives
  - synthetic-speech
pretty_name: "HEAR: Hierarchical Evaluation of Attribution and Reasoning"
size_categories:
  - 1K<n<10K
configs:
  - config_name: default
    data_files:
      - split: test
        path: data/test-*
extra_gated_heading: "Request access to HEAR and CASH-60K"
extra_gated_prompt: >-
  **This dataset contains synthetic speech in the voices of real, identifiable
  people.** One utterance inside each counterfactual clip was re-synthesised in
  another speaker's voice using voice cloning.


  The source corpora (AMI, ICSI, VoxMM) carry licences or research-use conditions
  for the original recordings, but those conditions **do not constitute consent to
  generate novel utterances in an individual's voice**, and no task-specific
  voice-cloning consent was obtained from the source speakers. We also do not treat
  pseudonymous speaker ids as full anonymity, because a recognisable voice itself
  carries identity. For that reason the synthetic audio is released as a controlled
  research resource rather than an unrestricted download.


  ### Data Use Agreement


  By requesting access you agree that you will:


  1. Use the synthetic audio **only for non-commercial research** on speaker-aware
     speech understanding and reasoning.

  2. **Not redistribute** the audio or any derivative of it, in whole or in part.

  3. **Not attempt to re-identify** the source speakers.

  4. **Not use the audio for impersonation**, deceptive or misleading content, fraud,
     harassment, or any use intended to harm or misrepresent an individual.

  5. **Not use it for commercial voice replication**, and not deploy cloned voices in
     interactive or production systems.

  6. Treat every synthetic utterance as **artificially generated**. These are not
     statements actually made by the corresponding speakers and must not be presented
     as representing their views, intentions, beliefs, or endorsements.


  Access is revocable if these conditions are violated.


  ### Takedown


  Rights holders, individuals whose voice appears in the data, and their authorised
  representatives may request removal. Identify the source recording or give enough
  information to locate the affected samples; valid requests are removed from
  subsequent distributions. Takedown records are kept in the dataset documentation
  without recording unnecessary personal information about the requester.
extra_gated_fields:
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    options:
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extra_gated_button_content: "Agree and request access"
---

# HEAR

🎉 **EMNLP 2026 main conference** 🎉

[📄 **arXiv**](https://arxiv.org/abs/2608.29120) &nbsp;·&nbsp;
[🌐 **Project page**](https://attributetoreason.github.io/AttributeToReason/) &nbsp;·&nbsp;
[💻 **Code**](https://github.com/dwsmart32/HEAR) &nbsp;·&nbsp;
[🤗 **Dataset**](https://huggingface.co/datasets/PleasedPenguin/HEAR) &nbsp;·&nbsp;
[🧠 **Model**](https://huggingface.co/PleasedPenguin/A2R-30B-A3B)

**H**ierarchical **E**valuation of **A**ttribution and **R**easoning, a benchmark for
*speaker-attributed* understanding of multi-party speech.

Most speech benchmarks can be solved by transcribing the audio and reading the text. HEAR
cannot. Every question asks something about **who** is speaking, not only **what** is said,
and roughly half the benchmark comes in **counterfactual pairs**: the same clip, the same
question, the same five options, but one utterance has been re-voiced in a different
speaker's voice, which **flips the correct answer**. A model that only reads the transcript
answers both members of a pair identically and scores zero on the pair.

```python
from datasets import load_dataset

hear = load_dataset("PleasedPenguin/HEAR", split="test")
row = next(r for r in hear if r["subtaxonomy"] == "VC")

row["audio"]       # the prompt audio, exactly what the model should hear
row["question"]    # "How many distinct speakers are present in this audio clip?"
row["options"]     # ['5', '2', '6', '3', '4']
row["answer_idx"]  # 3   -> option "3", letter "D"
row["answer"]      # "(D) 3"
```

---

## Taxonomy

Three axes → eight sub-dimensions. Descriptions follow the paper.

### Discrimination, *tell voices apart and locate them in time*

| | Sub-dimension | What it asks | n |
|---|---|---|---:|
| `VC` | **Voice Cardinality** | How many unique speakers are present in the clip | 190 |
| `VL` | **Voice Localization** | Given a reference voice, which time range does that speaker **appear** in, or **not appear** in | 237 |
| `VCD` | **Voice Change Detection** | Which time range contains a **speaker transition** (non-overlap) or **simultaneous speech** (overlap) | 200 |

### Attribution, *bind content to identity, in both directions*

| | Sub-dimension | What it asks | n |
|---|---|---|---:|
| `CVA` | **Content-to-Voice Attribution** | Given an utterance as text, pick the speaker from **five candidate voices** | 307 |
| `VCA` | **Voice-to-Content Attribution** | Given a reference voice, pick which of five texts that voice spoke | 301 |

### Reasoning, *track "who said what" across the conversation*

| | Sub-dimension | What it asks | n |
|---|---|---|---:|
| `IR` | **Identity Reasoning** | Given an anchor utterance, find another utterance by the **same** speaker | 370 |
| `QR` | **Quantitative Reasoning** | Aggregate per-speaker statistics, count speakers who said a phrase (`QR1`), or rank speakers by total speaking time (`QR2`) | 560 |
| `TR` | **Temporal Reasoning** | Order utterances by speaker, a speaker's **first**/**last** utterance (`TR1_first`, `TR1_last`), or what they said **after** an anchor by someone else (`TR2`) | 230 |

### `task` and `overlap`

`task_name` names the sub-task inside a sub-dimension, and is `none` where a sub-dimension has
only one form (`VC`, `CVA`, `VCA`, `IR`):

`change` · `overlap` (VCD), `appear` · `notappear` (VL), `QR1` · `QR2`, `TR1_first` · `TR1_last` · `TR2`

`overlap` says whether answering **requires** attending to overlapping speech. It is
annotated by human reviewers for every dimension **except Voice Cardinality**, where it is
`null`. For `VCD` the two `task_name` values *are* the two regimes, so `change` → `nonoverlap`
and `overlap` → `overlap`.

---

## The counterfactual pairs

`variant` is `original` or `hallucinated` for the 1,160 Reasoning rows, and `none` elsewhere.
Twins share everything but the voice of one utterance:

```python
reasoning = hear.filter(lambda r: r["variant"] != "none")

# a pair, recovered from the id
base = "Bmr019_spk5_len111s_31b1_IR_overlap_fe008_76.3_mn017"
orig = hear.filter(lambda r: r["question_id"] == base + "_original")[0]
hall = hear.filter(lambda r: r["question_id"] == base + "_hallucinated")[0]

orig["question"] == hall["question"]      # True
orig["options"]  == hall["options"]       # True
orig["answer_idx"], hall["answer_idx"]    # 0, 2   <- the voice swap moved the answer
```

There are **580 pairs, all complete**, every `_original` has its `_hallucinated`, and no row
is an orphan.

---

## Columns

| column | type | notes |
|---|---|---|
| `question_id` | string | primary key; a Reasoning id ends in `_original` / `_hallucinated` |
| `clip_id` | string | 887 clips; several questions share one clip |
| `source_corpus` | string | `ami` · `icsi` · `voxmm` |
| `taxonomy` | string | `Discrimination` · `Attribution` · `Reasoning` |
| `subtaxonomy` | string | the eight above |
| `task_name` | string | sub-task, `none` where there is only one |
| `overlap` | string | `nonoverlap` · `overlap` · `null` (Voice Cardinality only) |
| `variant` | string | `original` · `hallucinated` · `null` |
| `question` | string | the question alone |
| `options` | list[string] | five options, already shuffled, **this order is authoritative** |
| `answer_idx` | int32 | 0-based index into `options` |
| `answer` | string | `"(D) 3"`, letter and text together |
| `audio` | Audio 16 kHz | **the prompt**, the clip plus whatever reference audio the task needs |
| `option_audio_A` … `option_audio_E` | Audio 16 kHz | the five candidate **voices**, one column each so the viewer plays them; set for Content-to-Voice Attribution, `null` elsewhere |
| `answer_audio` | Audio 16 kHz | the correct voice, for the same rows, `null` wherever the answer is text |

### Three prompt layouts, all pre-assembled in `audio`

| layout | `audio` contains | sub-dimensions |
|---|---|---|
| main only | the clip | VC · VCD · IR · QR · TR |
| main + reference | clip, then the target speaker's voice | VL · VCA |
| main + five voices | clip, then "A" ⟨voice⟩ … "E" ⟨voice⟩ | CVA |

You never have to assemble anything: `audio` is the exact waveform used in the paper.
The per-option voice columns are provided separately so you can rebuild a different prompt format if you want.

---

## Scoring

Plain accuracy for Discrimination and Attribution. Reasoning is scored twice:

```python
# per-item accuracy, counting only the ORIGINAL member of each pair
# pair-strict: a pair scores 1 only if BOTH members are correct
```

The paper's headline metric, **Ver-A**, treats a counterfactual pair as **one unit**:

```
units  =  1,235 non-Reasoning items  +  580 pairs  =  1,815
```

Pair-strict scoring is what separates voice-grounded models from transcript-reading ones.
Strong text-centric models lose 30–50 points when they move from per-item to pair-strict.

---

## Source data

Clips are drawn from three corpora with human-produced transcripts, segmented to 30–150 s
and filtered to multi-party regions.

| corpus | clips | source recordings | speakers | mean length | hours |
|---|---:|---:|---:|---:|---:|
| VoxMM (YouTube) | 360 | 34 | 245 | 87.8 s | 8.78 |
| AMI (meetings) | 241 | 23 | 24 | 97.4 s | 6.52 |
| ICSI (meetings) | 286 | 6 | 21 | 94.9 s | 7.54 |
| **total** | **887** | **63** | **290** | **92.7 s** | **22.84** |

Every question was reviewed by human annotators for solvability, a unique correct answer,
voice-identity preservation in the swapped clips, and transcript–option consistency.

> **Note on counts.** The paper reports 2,575 questions over 990 clips. This release is the
> camera-ready revision: Ego4D was removed for licensing reasons (159 questions, 103 clips)
> and 21 further items were dropped so that every counterfactual pair is complete and every
> row's audio matches its metadata. Hence **2,395 questions over 887 clips**.

---

## Also in this repo: CASH-60K, the training corpus

`CASH-60K/` holds the corpus **A2R** was trained on, 59,762 queries built from the VoxMM
*train* split, with **hard negatives** made the same way HEAR's counterfactuals are: one
utterance re-voiced as another speaker, so the words are unchanged and the answer
moves.

It is kept out of the dataset viewer on purpose, the viewer is for the benchmark. Because it
carries columns the benchmark does not, load it as parquet rather than by repo id, otherwise
`datasets` tries to cast it into the benchmark's schema:

```python
from datasets import load_dataset

cash = load_dataset(
    "parquet",
    data_files="hf://datasets/PleasedPenguin/HEAR/CASH-60K/train-*.parquet",
    split="train",
)
```

Same column names as the benchmark, plus two, and with no `answer_audio`:

| column | notes |
|---|---|
| `variant` | `original` · `hard_negative` |
| `hn_version` | which swap recipe produced a hard negative (`v0`–`v7`, `real_v5`); `null` on originals |
| `pair_id` | groups an original with its 1–3 hard negatives |

| | queries |
|---|---:|
| original | 25,943 |
| hard negative | 33,819 |
| **total** | **59,762** |

Built from 5,242 source clips plus 14,180 voice-swapped variants (19,422 clips total).
Every synthesized swap was screened by ASR word-error-rate against the intended transcript,
so only the voice moved. CASH draws from VoxMM **train**; HEAR draws from VoxMM **test** plus
AMI and ICSI, **no clip overlap**.

---

## Also in this repo: the three OOD benchmarks

`ood_benchmark/` holds the three held-out benchmarks A2R is evaluated on **zero-shot**. None
of them is drawn from the corpora CASH-60K is built on, and in all three the answer turns on
voice identity rather than on the transcript.

| file | task | questions | clips | hours | mean clip | options |
|---|---|---:|---:|---:|---:|---:|
| `wdyl-*.parquet` | a two-speaker dialogue, then a first-person question asked in one of the two voices | 793 | 793 | 2.36 | 10.7 s | 3 |
| `gaokao-*.parquet` | a listening-exam conversation, then a first-person question in one speaker's voice | 93 | 93 | 0.52 | 20.0 s | 2 |
| `fts-*.parquet` | Find the Spy: 3-6 players introduce themselves, then give clues in a reshuffled order without names | 400 | 400 | 3.67 | 33.1 s | 3-6 |

1,286 questions and 6.55 hours in total, all multiple choice with one gold option. Columns are
`question_id`, `benchmark`, `audio`, `question`, `options`, `answer_idx`, `answer`,
`instruction`. Audio decodes to 16 kHz mono. `instruction` carries the task description and
the options and stops there, so you are free to append whatever output format your model
expects; the paper's own numbers were produced by appending a transcribe-then-reason
instruction to it.

```python
from datasets import load_dataset

fts = load_dataset(
    "parquet",
    data_files="hf://datasets/PleasedPenguin/HEAR/ood_benchmark/fts-00000-of-00001.parquet",
    split="train",
)
```

---

## Ethical considerations

**This resource contains synthetic speech in the voices of real people.** Access is gated for
that reason; the terms are in the access form and summarised here.

**Dual use.** Voice cloning and speaker attribution can be misused for impersonation, deceptive
audio, fraud, harassment, or speaker re-identification. We therefore treat the synthetic speech
as a controlled research resource, not an unrestricted public download.

**Voice, likeness, consent.** The source corpora license the *original* recordings. That is not
consent to generate *new* utterances in someone's voice, and no task-specific voice-cloning
consent was obtained from the source speakers. A recognisable voice carries identity on its own,
so pseudonymous speaker ids are not treated as anonymity.

**What is open and what is gated.** The code, the evaluation protocol, the annotations and the
aggregate statistics are public. The synthetic waveforms are available only through this gated
repository, to approved non-commercial research, under the Data Use Agreement.

**Non-attribution.** Every synthetic utterance is an artificially generated sample. It is not
something the corresponding speaker said, and must not be presented as their view, intention or
endorsement. Identifying metadata that the research does not need, including speaker names and
source-speaker mappings, has been removed.

**Takedown.** Rights holders and individuals whose voice appears here, or their authorised
representatives, can request removal; valid requests are dropped from subsequent distributions.

---

## Citation

```bibtex
@misc{lee2026hearsaidwhatunlocking,
  title         = {HEAR Who Said What: Unlocking Speaker-Attributed Reasoning
                   via Counterfactual Voice Grounding},
  author        = {Dongwook Lee and Sangkwon Park and Eunwoo Song and Che Hyun Lee
                   and Youngho Cho and Junho Kim and June Young Yi and Heeseung Kim
                   and Sungroh Yoon},
  year          = {2026},
  eprint        = {2608.29120},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CL},
  url           = {https://arxiv.org/abs/2608.29120}
}
```

## License

Released under CC BY-NC 4.0. The underlying corpora keep their own licenses, AMI and ICSI
are distributed by their respective consortia, and VoxMM is built from license-free YouTube
material. Please honour those terms for any redistribution.