--- 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.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 `::`. 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} } ```