| --- |
| pretty_name: MultiPartyTurnBench |
| license: cc-by-4.0 |
| language: |
| - en |
| task_categories: |
| - audio-classification |
| - voice-activity-detection |
| tags: |
| - turn-taking |
| - multi-party |
| - full-duplex |
| - end-of-turn |
| - interruption |
| - backchannel |
| - spoken-dialogue |
| - synthetic-speech |
| - benchmark |
| size_categories: |
| - n<1K |
| configs: |
| - config_name: default |
| data_files: |
| - split: dev |
| path: data/dev.parquet |
| - split: test |
| path: data/test.parquet |
| --- |
| |
| # MultiPartyTurnBench (MPTB) |
|
|
| **MultiPartyTurnBench: Benchmarking Floor Control in Multi-Party Spoken Dialogues** |
|
|
| MultiPartyTurnBench is a controlled benchmark for **multi-party conversational floor control**. It contains 240 |
| synthetic, channel-separated conversations with 2–5 speakers and six interaction types, each with an **exact, |
| role-relative event ground truth labels**. The log records who holds the floor, when a speaker yields, pauses or is cut off, and |
| which listener speech successfully takes the floor or leaves it unchanged. Two causal tasks are defined on this |
| log: |
|
|
| - **End-of-Turn (EOT):** detect when the current floor holder has *yielded* the floor (positives), and do not |
| fire during *mid-turn hold pauses* after which the same speaker resumes (hard negatives). |
| - **Interruption (INT):** detect when a listener *successfully takes* the floor while another speaker holds it |
| (positives), and do not fire on *backchannels or laughter* that leave the floor unchanged (hard negatives). |
| Failed floor claims are excluded from scoring. |
|
|
| This repository contains the complete benchmark data: per-speaker audio (noisy evaluation input and clean |
| reference), ground-truth labels, derived scoring anchors, the dev/test split and all metadata needed for |
| evaluation. The **evaluation code** (scorer and operating-point selection) is released separately on GitHub. |
|
|
| --- |
|
|
| ## 1. Quick start |
|
|
| **Download.** |
| ```python |
| from huggingface_hub import snapshot_download |
| root = snapshot_download("puneetUMD/MultiPartyTurnBench", repo_type="dataset") |
| ``` |
| or `huggingface-cli download puneetUMD/MultiPartyTurnBench --repo-type dataset --local-dir MultiPartyTurnBench`. |
|
|
| **Load a conversation for evaluation.** |
| ```python |
| import json, os, soundfile as sf |
| dev = [json.loads(l) for l in open(os.path.join(root, "dev/manifest.jsonl"))] |
| c = dev[0] |
| events = json.load(open(os.path.join(root, c["events"]))) # ground-truth event log |
| anchors = json.load(open(os.path.join(root, c["anchors"])))["anchors"] # scoring anchors per speaker |
| audio = {spk: sf.read(os.path.join(root, p))[0] for spk, p in c["audio_noisy"].items()} # 24 kHz mono per speaker |
| ``` |
|
|
| **Browse.** The dataset viewer on this page shows every conversation with a mixdown preview and its metadata |
| (`load_dataset("puneetUMD/MultiPartyTurnBench")` loads the same preview table; it is not the evaluation input). |
| For a richer offline view, download the repository and open `viewer/index.html` in a recent browser. It shows a |
| clickable per-speaker timeline coloured by ground-truth label, yield and interruption markers, hold pauses, the |
| floor-holder track, per-speaker clean and noisy channels and the transcript synchronized to playback. |
|
|
| **Score a system.** With the GitHub evaluation code (Python ≥ 3.8, standard library only): |
| ```bash |
| python eval/score.py score --root MultiPartyTurnBench --split test --submission my_test_predictions.json |
| python eval/score.py select --root MultiPartyTurnBench --split dev --submission 0.5=dev_0.5.json 0.7=dev_0.7.json |
| ``` |
| `select` applies the benchmark's operating-point rule: the highest recall with dev FPR ≤ 0.10, chosen per |
| track. Freeze the selected threshold and run `score` on `test`. A system is *within budget* if dev FPR ≤ 0.10 and |
| test FPR ≤ 0.15. |
|
|
| --- |
|
|
| ## 2. Contents |
|
|
| ``` |
| MultiPartyTurnBench/ |
| ├── README.md this dataset card |
| ├── DATASHEET.md datasheet for datasets |
| ├── LICENSE.md licenses and third-party notices |
| ├── SHA256SUMS checksums of every file |
| ├── dev/ development split (48 conversations; tune thresholds here only) |
| │ ├── manifest.jsonl one line per conversation with root-relative paths to all its files |
| │ ├── conversations.csv per-conversation metadata for this split |
| │ ├── audio/noisy/<id>_speaker_<k>.flac EVALUATION INPUT: per-speaker channels with the shared acoustic scene |
| │ ├── audio/clean/<id>_speaker_<k>.flac same channels before the noise stage, for inspection |
| │ └── annotations/ |
| │ ├── events/<id>.json ground-truth event log + conversation metadata (canonical gold) |
| │ ├── anchors/<id>.json EOT/INT positives, hard-negative spans and excluded spans per speaker (derived) |
| │ ├── scripts/<id>.json tagged dialogue script (text, event type, timing hint) produced in Stage 1 |
| │ └── render/<id>.json placement of every utterance on the timeline (start/end, truncation) + noise manifest |
| ├── test/ test split (192 conversations), same structure as dev/ |
| ├── metadata/ |
| │ ├── split.json 48 dev / 192 test conversation ids |
| │ ├── conversations.csv all 240 conversations: split, N, type, domain, tone, duration, voices, noise, event counts, QC |
| │ ├── voices.csv 24 synthetic voices (Kokoro voice, gender, style, dev/test pool) |
| │ ├── dataset_stats.json corpus statistics overall, by N and by interaction type |
| │ ├── qc_report.json automatic acoustic quality-control results per conversation and channel |
| │ └── noise_seeds.json noise-stage seed per conversation (reproduces audio/noisy from audio/clean) |
| ├── generation/ |
| │ ├── stage1_system_prompt.txt verbatim LLM system prompt |
| │ ├── stage1_user_prompt_template.txt user prompt template and repair message |
| │ └── generation_config.json all generation parameters (Stages 1–5) |
| ├── data/{dev,test}.parquet dataset-viewer preview: metadata, mixdown preview audio and the event log per conversation |
| └── viewer/ |
| ├── index.html offline dataset viewer |
| ├── data.js viewer data (derived from the annotations) |
| └── mix/<id>.mp3 64 kbps mono preview mixdowns of the noisy channels (not an evaluation input) |
| ``` |
|
|
| Conversation ids are `dev_00001`–`dev_00048` and `test_00001`–`test_00192`. Speakers are `speaker_1` … |
| `speaker_N`. All times are in seconds on the shared conversation timeline. All channels of a conversation are |
| sample-aligned and have the same length. Audio is 24 kHz, mono, 16-bit FLAC (1,680 files: 840 noisy + 840 clean). |
| The viewer and the paper figures label speakers S1…SN. Paths in `manifest.jsonl` and in the preview table are |
| relative to the dataset root. |
|
|
| --- |
|
|
| ## 3. Composition |
|
|
| | | Conv. | Hours | EOT + / − | INT + / − | Failed claims (excl.) | |
| |---|---:|---:|---:|---:|---:| |
| | dev | 48 | 1.42 | 706 / 55 | 86 / 309 | — | |
| | test | 192 | 5.47 | 2,716 / 186 | 281 / 1,172 | — | |
| | **total** | **240** | **6.89** | **3,422 / 241** | **367 / 1,481** | **605** | |
|
|
| | N | Conv. | Hours | Mean turn (s) | Events/min | BC/min | INT/min | Overlap / dialogue (s) | |
| |---|---:|---:|---:|---:|---:|---:|---:| |
| | 2 | 60 | 1.90 | 7.07 | 13.8 | 3.12 | 0.70 | 10.2 | |
| | 3 | 60 | 1.66 | 6.38 | 16.3 | 3.61 | 0.96 | 10.8 | |
| | 4 | 60 | 1.67 | 6.10 | 16.3 | 3.60 | 0.94 | 10.9 | |
| | 5 | 60 | 1.66 | 6.26 | 16.6 | 4.06 | 0.97 | 11.0 | |
|
|
| - Every (N, interaction type) cell has exactly 10 conversations: 2 dev and 8 test. |
| - The interaction types are casual, task-oriented, instructional, collaborative, argumentative and narrative. |
| - The six annotated event classes total 6,116 events: 3,422 yields, 241 holds, 367 interruptions, 605 failed |
| claims, 1,176 backchannels and 305 laughter vocalizations. |
| - In addition, 367 holder turns are cut off by interruptions. |
| - Conversations last 48–208 s (median 100 s). |
| - Development and test use **disjoint voice pools** (8 and 16 voices, gender-balanced), and no voice repeats |
| within a conversation. |
|
|
| --- |
|
|
| ## 4. Annotation format |
|
|
| ### 4.1 Event log (`<split>/annotations/events/<id>.json`) |
| ```json |
| {"conversation_id": "test_00013", "conversation_type": "narrative", "num_speakers": 5, |
| "speakers": ["speaker_1", ..., "speaker_5"], "duration_s": 157.529, |
| "audio": {"mode": "channel_separated", "files": {"speaker_1": "test_00013_speaker_1.flac", ...}}, |
| "metadata": {"domain": ..., "voices": {...}, "voice_styles": {...}, "noise_profile": {...}, |
| "sample_rate": 24000, "tone": ..., "generator_model": "gpt-4.1", "engine": "kokoro"}, |
| "events": [ |
| {"id": "e004", "speaker": "speaker_1", "label": "TURN", "onset": 0.0, "offset": 19.19, |
| "current_holder": "speaker_1", "floor_outcome": "yield", "next_holder": "speaker_2"}, |
| {"id": "e001", "speaker": "speaker_2", "label": "VOCALIZE_NO_CLAIM", "sublabel": "BACKCHANNEL", |
| "onset": 7.965, "offset": 9.175, "current_holder": "speaker_1"}, |
| {"id": "e003", "speaker": "speaker_4", "label": "CLAIM_FLOOR", "resolved_as": "NON_FLOOR_TAKING_INTERRUPTION", |
| "onset": 14.88, "offset": 16.21, "current_holder": "speaker_1"}, |
| ...]} |
| ``` |
|
|
| | `label` | qualifier | meaning | role | |
| |---|---|---|---| |
| | `TURN` | `floor_outcome: yield` (+ `next_holder`) | holder releases the floor (or the conversation ends) | EOT positive at `offset` | |
| | `TURN` | `floor_outcome: hold` | holder pauses mid-turn and resumes | EOT hard negative over [`offset`, resume onset] | |
| | `TURN` | `floor_outcome: interrupted` | holder is cut off by a successful interruption | EOT excluded (±0.25 s) | |
| | `CLAIM_FLOOR` | `resolved_as: INTERRUPTION` (+ `floor_transfer_time`) | listener takes the floor | INT positive at `onset` | |
| | `CLAIM_FLOOR` | `resolved_as: NON_FLOOR_TAKING_INTERRUPTION` | listener tries to take the floor and fails | INT excluded over [`onset`, `offset`] | |
| | `VOCALIZE_NO_CLAIM` | `sublabel: BACKCHANNEL` / `LAUGHTER` | listener vocalization without a floor claim | INT hard negative over [`onset`, `offset`] | |
|
|
| Acoustic nuisance (ambient bed, sound effects, distant third-party speech) is described in |
| `metadata.noise_profile` and the render noise manifest. It never creates anchors. |
|
|
| ### 4.2 Anchors (`<split>/annotations/anchors/<id>.json`) |
| For every speaker channel and track, the file lists `positives` (anchor times), `negative_spans` and |
| `excluded_spans`. It is derived from the event log by the anchor-derivation module of the evaluation code (`mptb_eval/anchors.py`), and the scorer uses exactly |
| these anchors. |
|
|
| ### 4.3 Script and render |
| - `<split>/annotations/scripts/<id>.json` holds the Stage-1 dialogue: each utterance's `speaker`, `text`, `event_type`, |
| `timing_hint` and, where applicable, `target`, `resolved_hint` and `sublabel`. It also records the generation |
| `targets` and `tone`. |
| - `<split>/annotations/render/<id>.json` lists every placed clip: `speaker`, `start_s`, `end_s`, `kind` |
| (`holder` / `claim` / `backchannel` / `laughter`), `overlaps`, `truncated`, and `src_index` (the index into the |
| script). It also holds the realized noise manifest (`noise`). |
| - Each clip is one script utterance, so script text can be aligned to audio through `src_index`. |
|
|
| --- |
|
|
| ## 5. Scoring protocol |
| 1. **Submission.** For every conversation and speaker channel, a system submits causal decision times for each |
| track. Systems that output scores are converted to decisions by taking rising edges above θ with a 2 s |
| refractory period. |
| 2. **Matching** (`mptb_eval/scorer.py` in the evaluation code, following TurnBench): |
| - Predictions inside excluded spans are dropped. |
| - Each gold positive at time *t* is matched to the earliest unclaimed prediction in [t − 0.25, t + 3.0] s, |
| counting as a TP if found and an FN otherwise. |
| - Each hard-negative span counts as one FP if any remaining unclaimed prediction falls inside it, and one TN |
| otherwise. |
| - Signed latency is t_pred − t_gold. |
| 3. **Metrics.** Recall = TP/(TP+FN) and FPR = FP/(FP+TN), pooled over all speakers and conversations of a split. |
| 4. **Operating point.** θ maximizes dev recall subject to dev FPR ≤ 0.10, chosen per track, and is frozen for |
| test. A test FPR above 0.15 means the system is out of budget. |
| |
| --- |
|
|
| ## 6. Intended use and limitations |
| - **Intended use:** diagnostic evaluation of causal end-of-turn and interruption detectors and of full-duplex |
| agents in controlled multi-party conditions, with breakdowns by N and interaction type. |
| - **Not intended for:** estimating error rates on natural conversation, certifying deployment safety, or speaker |
| identification. All speech is synthetic and all participants are fictional. |
| - **Limitations:** |
| - The corpus is English-only. |
| - The prosody, voice timbre and disfluency of a single TTS model do not cover natural variation. |
| - Timing comes from parametric distributions. |
| - Hold pauses are inserted silences of 0.4–1.2 s. |
| - Dialogue content reflects the generating LLM. |
|
|
| See `DATASHEET.md` for details. |
|
|
| ## 7. License and citation |
| - **Data:** CC BY 4.0. **Viewer code:** MIT. Third-party notices (Kokoro-82M, MUSAN, `gpt-4.1`, TurnBench) are in |
| `LICENSE.md`. |
| - If you use MultiPartyTurnBench, please cite: |
|
|
| ```bibtex |
| @misc{multipartyturnbench2026, |
| title = {MultiPartyTurnBench: Benchmarking Floor Control in Multi-Party Spoken Dialogues}, |
| author = {Puneet Mathur, Dinesh Manocha}, |
| year = {2026}, |
| note = {Dataset: https://huggingface.co/datasets/puneetUMD/MultiPartyTurnBench} |
| } |
| ``` |
|
|
