README.md

---
pretty_name: MAD2
license: other
license_name: mad2-noncommercial-research-1.0
license_link: LICENSE
language:
- en
task_categories:
- audio-classification
- text-classification
- automatic-speech-recognition
tags:
- synthetic-speech
- conversational-audio
- check-worthiness
- misinformation
size_categories:
- 1K<n<10K
configs:
- config_name: default
  default: true
  data_files:
  - split: corpus
    path:
    - audio/*.wav
    - audio/metadata.jsonl
---

# MAD2

MAD2 contains 1,000 synthetic English conversations, their original written
dialogues, ASR transcripts of the replacement recordings, and current sentence
check-worthiness labels. Audio was generated with MoonCast using `alba`,
`ears-p015`, `ears-p016`, and `expresso-ex01` reference voices.

The package contains 11.04 hours of mono 24 kHz PCM16 audio and 8,192 labeled
sentences: 1,230 check-worthy and 6,962 negative. Each dialogue has two distinct
voices; each reference voice occurs in 500 dialogues.

This is the compact local release package, not a record of public-release approval.
Sentence forced alignment is not included: sentence start/end times remain null.
The dataset release version remains to be finalized. The paper citation is
provided below.

## Audio Release and Re-Synthesis

The paper's experiments and WER evaluation use the original recordings generated
with MoonCast's two default demonstration voices. MoonCast's
[speech-generation notice](https://github.com/jzq2000/MoonCast#speech-generation)
prohibits redistribution of its bundled demonstration audio prompts and audio
generated from those prompts. We therefore regenerated the recordings for public
release using the four alternative reference voices listed above. The original
default-voice recordings are not distributed in this package.

The released audio is a replacement corpus: the paper's reported model
performance and WER do not evaluate these replacement recordings.

Researchers are welcome to use the scripts in `dialogues/original.jsonl` to
generate their own audio with other TTS systems and voices for noncommercial
research, subject to the [MAD2 license](LICENSE), applicable third-party terms,
and the permissions required by their chosen TTS models and voices. Please
identify the synthesis system and voices used and distinguish any new recordings
from the supplied MAD2 audio. New recordings require their own ASR transcripts
and timing alignments; timestamps from the supplied audio should not be reused.

## Files

```text
MAD2_release/
  README.md
  LICENSE
  THIRD_PARTY_NOTICES.md
  VOICE_ATTRIBUTION.md
  licenses/
  dialogues/
    original.jsonl
  audio/
    <dialogue_id>.wav
    metadata.jsonl
  asr/
    transcripts.jsonl
  annotations/
    labels.jsonl
```

The four JSONL files each contain 1,000 rows, joined by the string `dialogue_id`.
There is one WAV per dialogue. JSONL is UTF-8 with one JSON object per line.
No train/dev/test partition is prescribed; `corpus` names the complete collection,
not a training split. Researchers must specify their own evaluation partition and
keep all sentences from a dialogue together when splitting.

## Original Dialogues

`dialogues/original.jsonl` stores the written dialogue text once, preserving the
sentence strings, speaker roles and turn boundaries from the hash-verified server
scripts. It does not include the old script labels. Fields:

- `dialogue_id`, `source_dataset`, `source_claim_id`: corpus and LIAR source identity.
- `scenario_type`, `spread_style`: corrected server categories.
- `source_script_sha256`: hash of the original source JSON, not this packaged row.
- `turns`: `original_turn_idx`, `role`, and `sentences`. Each sentence has
  `sentence_id`, `original_sent_idx`, and `text`.
- `audio_script`: compact text edits and turn spans that map the original dialogue
  to the exact text used to synthesize the released recording, without storing
  another full dialogue copy.

Indices are zero-based. A sentence ID is
`<dialogue_id>:<original_turn_idx>:<original_sent_idx>`. Roles `0` and `1` refer to
the voice assignments in audio metadata, not automatically inferred diarizer IDs.

### Audio-Script Differences

Nine dialogues have text edits: seven added acknowledgments and two apostrophe
corrections. Twelve inputs merge adjacent same-role turns. The original text and
current sentence IDs are preserved; generated and original turn indices differ.

To interpret `audio_script`, first join the original sentence strings in order
with one ASCII space. `text_edits` contains ordered, nonoverlapping
`char_start`, `char_end`, `replacement` patches against that string. Apply them
from last to first. The resulting string is the exact joined synthesis text.
Then each `turn_spans` entry selects a generated turn using `generated_turn_idx`,
`role`, `char_start`, and `char_end` in the resulting string.

All character spans are half-open Python Unicode-character offsets, not UTF-8
byte offsets. These are text mappings, not audio timestamps.

## Audio

`audio/metadata.jsonl` contains `file_name`, `dialogue_id`, `duration_seconds`,
`sample_rate`, `audio_sha256`, `synthetic`, `engine`, `role_0_voice`, and
`role_1_voice`. `file_name` is relative to the audio directory. No full script,
ASR transcript, or sentence-label table is duplicated in this metadata.

All 1,000 WAVs are the unchanged approved replacement takes. 979 passed automatic
QA; the remaining 21 were accepted by the dataset owner, including seven exact
takes reviewed by listening and 14 accepted as minor differences. This acceptance
does not mean every ASR word or diarization boundary is correct.

## ASR

`asr/transcripts.jsonl` contains `dialogue_id`, the recognized `transcript`, and
`words`. Word entries preserve `word`, `start`, `end`, and `probability` from the
new-audio faster-whisper large-v3 reports. Times are seconds relative to the WAV.
The transcript and word sequence are two views of ASR output, not corrected script
text. Word times/confidences are automatic, not human gold annotations; words
are not assigned speaker identities in this table.

## Labels

`annotations/labels.jsonl` stores one row per dialogue with:

- `source_claim_veracity`: inherited binary LIAR seed label (`true` or `false`),
  not a truth judgment about every utterance.
- `source_claim_original_label`: original LIAR `true`, `false`, or `pants-fire`.
  `false` and `pants-fire` map to binary `false`; `true` remains `true`.
- `annotation_version`: `server_active_aligned_20260913`, identifying the current
  server label layer. The name does not assert alignment to the new recordings.
- `sentences`: `sentence_id`, `check_worthy`, `generated_text_spans`,
  `audio_start`, `audio_end`, and `alignment_status`.
- `audio_only_turns`: the seven synthesis-only acknowledgments, identified by
  `generated_turn_idx`, with `check_worthy: null` and
  `annotation_status: synthesis_only_addition_unlabeled`.

`check_worthy` is a selection label, not factual correctness. The current 1,230
positive labels are retained, not the old 3,368 positives. Current does not imply
human-verified. Audio-only acknowledgments are not counted among the 8,192 labeled
sentences and must not silently be scored as negative.

`generated_text_spans` selects text within the reconstructed generated turn:
`generated_turn_idx`, `char_start`, and `char_end`. Matching ignores case and
punctuation, enforces the speaker role, and can omit boundary punctuation. It is
not forced alignment. `audio_start` and `audio_end` are null, with status
`pending_new_audio_sentence_alignment`; no old-audio sentence times are reused.

## Loading

The default [AudioFolder](https://huggingface.co/docs/datasets/audio_dataset)
view loads audio and minimal metadata only. Load the other tables separately and
join on `dialogue_id`; sentence text and labels join on `sentence_id`.

```python
from datasets import load_dataset

audio = load_dataset("./MAD2_release", split="corpus")
dialogues = load_dataset("json", data_files={
    "corpus": "./MAD2_release/dialogues/original.jsonl"
}, split="corpus")
asr = load_dataset("json", data_files={
    "corpus": "./MAD2_release/asr/transcripts.jsonl"
}, split="corpus")
labels = load_dataset("json", data_files={
    "corpus": "./MAD2_release/annotations/labels.jsonl"
}, split="corpus")
```

No custom loader or experimental code is required in the repository. Local
validation was performed with `datasets` 3.6.0; decoding dependencies depend on
the installed library version.

## Scope and Limitations

MAD2 supports research on conversational check-worthiness and misinformation.
These are synthetic dialogues, not authentic conversations or evidence that a
reference performer made the statements. Content can be false, political,
misleading or offensive. Inherited seed labels and automatic annotations are not
independent fact-checking evidence. The four-voice synthetic setting and source
bias limit generalization. Earlier results on the original audio do not measure
this replacement corpus.

Experimental splits, old labels, context summaries, diagnostic QA/diarization
reports, generation logs, validation scripts, and release-preparation records are
kept locally, outside this dataset package. Original restricted MoonCast audio,
reference WAVs, LIAR TSVs, credentials, model weights and runtime code are not
distributed here.

## License and Attribution

MAD2-controlled contributions use the [MAD2 Noncommercial Research License
1.0](LICENSE), permitting the specified noncommercial research uses only within
the licensor's controlled rights. Upstream rights remain separate. Alba's source
recording is CC BY 4.0; EARS and Expresso references are CC BY-NC 4.0; LIAR retains
its source-copyright and research-use notice. Neither the custom license nor this
repackaging certifies public cloned-voice scope, source/service compliance, or
authority over any institutional/collaborator rights. Future license revisions
do not retroactively replace validly granted permissions.

See [source and method notices](THIRD_PARTY_NOTICES.md),
[voice attribution](VOICE_ATTRIBUTION.md), and the unchanged texts in `licenses/`.
LIAR: William Yang Wang (2017), ["Liar, Liar Pants on Fire": A New Benchmark Dataset
for Fake News Detection](https://aclanthology.org/P17-2067/).
Synthesis: [MoonCast](https://github.com/jzq2000/MoonCast).

Contact/licensor for controlled or authorized rights: Chaewan Chun,
czc5884@psu.edu.

## Citation

```bibtex
@inproceedings{chun-etal-2026-context-aware,
  author    = {Chun, Chaewan and Zhang, Delvin Ce and Lee, Dongwon},
  title     = {Context-Aware Multimodal Claim Verification in Spoken Dialogues},
  booktitle = {The 2nd Speech and Audio Language Models Workshop
               ({SALMA}), {EMNLP}},
  year      = {2026}
}
```