README.md

---
license: cc-by-nc-4.0
task_categories:
  - audio-classification
language:
  - en
tags:
  - speech
  - naturalness
  - dyadic-conversation
  - voice-conversion
  - text-to-speech
  - augmentation
size_categories:
  - 10K<n<100K
splits:
  - name: train
    num_examples: 5131
  - name: test
    num_examples: 3303
---

# TRACE — Temporal Relationship-Aware Conversational Entrainment Detection in Dyadic Speech

TRACE is a binary naturalness-labeled dataset of dyadic spoken interactions, designed for training and evaluating speech naturalness classifiers. Each sample pairs two speaker audio tracks with metadata about their relationship and conversational context.

The dataset contains **8,434 labeled dyadic pairs** across four augmentation conditions, split into train (5,131) and test (3,303) sets.

**Paper:** [arxiv.org/abs/2606.30543](https://arxiv.org/abs/2606.30543)

---

## Augmentation types

| `augmentation_type` | Description |
|---|---|
| `original` | Unmodified naturalistic speech from the Seamless Interaction corpus |
| `original_vc` | Speaker-A and Speaker-B tracks individually voice-converted via SeedVC |
| `emotivoice_tts` | Speaker-B replaced with EmotiVoice TTS conditioned on contrastive emotion |
| `emotivoice_tts_vc` | Same as `emotivoice_tts`, with Speaker-B TTS additionally voice-converted |

`naturalness = 1` for `original` pairs; `naturalness = 0` for all augmented conditions.

---

## Dataset structure

```
TRACE/
├── train.csv               # 5,131 rows with updated audio paths
├── test.csv                # 3,303 rows with updated audio paths
├── missing_files.txt       # 169 source files absent at collection time
└── audio/
    ├── original/           # naturalistic WAVs (from Seamless corpus)
    ├── original_vc/        # voice-converted naturalistic WAVs
    ├── emotivoice_tts/     # TTS-resynthesised Speaker-B WAVs + mixed track
    └── emotivoice_tts_vc/  # TTS + VC Speaker-B WAVs + mixed track
```

### CSV columns

| Column | Description |
|---|---|
| `row_id` | Unique row identifier |
| `augmentation_type` | See table above |
| `naturalness` | Binary label: 1 = natural, 0 = unnatural |
| `high_level_context` | Conversational context category |
| `participant1_relpath_abs` | Absolute path to Speaker-A audio |
| `participant2_relpath_abs` | Absolute path to Speaker-B audio |
| `rel_detail` | Speaker relationship description |
| `source_row_p1` | Source row ID for Speaker A |
| `source_row_p2` | Source row ID for Speaker B |
| `mixed_relpath_abs` | Path to mixed stereo track (TTS conditions only) |
| `transcript` | Transcript text (where available) |

---

## Source data and attribution

- **Seamless Interaction corpus** — Meta AI. Naturalistic dyadic speech recordings. [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/)
- **EmotiVoice** — NetEase Youdao. Emotion-conditioned TTS. [Apache 2.0](https://github.com/netease-youdao/EmotiVoice)
- **SeedVC** — Voice conversion model used for speaker identity transfer.

---

## License

This dataset is released under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) in accordance with the Seamless Interaction corpus license. Non-commercial use only.

---

## Citation

If you use this dataset, the pretrained weights, or the associated code in your research, please cite:

```bibtex
@misc{ugandhar2026tracetemporalrelationshipawareconversational,
      title={TRACE: Temporal Relationship-Aware Conversational Entrainment Detection in Dyadic Speech}, 
      author={Sathvik Manikantan Napa Ugandhar and Hao Zhang and Alison Gunzler and Yuzhe Wang and Thomas Thebaud and Georgi Tinchev and Venkatesh Ravichandran and Laureano Moro-Velázquez},
      year={2026},
      eprint={2606.30543},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2606.30543}, 
}
```