---
license: cc-by-4.0
language: [en]
pretty_name: "EmoSpoof-TTS"
task_categories: [audio-classification]
size_categories: [10K<n<100K]
configs:
  - config_name: default
    data_files:
      - {split: test, path: "data/test-*.parquet"}
tags:
  - anti-spoofing
  - audio-deepfake-detection
  - speech
  - benchmark
  - arena-ready
paperswithcode_id:
arxiv: ["2505.23962"]
---

# EmoSpoofTTS

A **spoof-only** attack corpus of emotional text-to-speech (TTS) synthesis:
36,000 clips spanning 3 modern TTS systems, 10 speakers, and 4 emotions, all
synthesized from transcripts of the Emotional Speech Dataset (ESD).

## Overview

EmoSpoof-TTS (Mahapatra et al., "Can Emotion Fool Anti-spoofing?", Interspeech
2025, arXiv:2505.23962) was built to study whether emotionally expressive TTS
is harder for anti-spoofing systems to detect than neutral TTS. For 10 ESD
speakers (`0011`-`0020`) and 4 emotions (Neutral, Angry, Sad, Happy), 300
held-out ESD transcripts per speaker/emotion were synthesized with three
zero-shot TTS systems:

- **StyleTTS2** (Li et al., NeurIPS 2023)
- **F5-TTS** (Chen et al., arXiv:2410.06885)
- **CosyVoice** (Du et al., arXiv:2407.05407)

This gives 3 models x 10 speakers x 4 emotions x 300 utterances = 36,000
synthetic clips (~29 hours), all parallel across models (same transcript per
`AUDIO_ID`, differing only by a `t{1,2,3}` model suffix).

### Spoof-only caveat

**This dataset contains no bonafide audio** (`n_bonafide = 0`). The
corresponding ESD bonafide recordings are a separate, independently licensed
corpus and are not redistributed here. As a result, EER computed against this
dataset alone is degenerate/one-sided (all trials are target=spoof), so the
Arena scores it with the **1-SRR metric (`srr_complement`, lower is better)**
instead — the fraction of these emotional-TTS spoofs **not rejected** at a
fixed operating threshold `t*` transferred from each model's own DeepVoice EER
operating point (carried in the submission's `calibration` block). It is an
**attack-difficulty probe**: it measures how often a detector calibrated
elsewhere mis-classifies these emotional-TTS spoofs as bonafide (its miss rate
on this specific attack family).

## License & redistribution

Released by the source authors under **Creative Commons Attribution 4.0
International (CC BY 4.0)**, per `/mnt/datasets/emospoof/README.txt` section
5 ("Copying"). The verbatim license text is in `LICENSE.txt`.

## Schema

| Field | Type | Description |
|---|---|---|
| path  | string | `{model}/{speaker}/{emotion}/<filename>.wav`, e.g. `StyleTTS2/0011/Neutral/0011_000001t1.wav`. |
| audio | Audio(16kHz mono) | Resampled on decode. |
| label | ClassLabel[bonafide, spoof] | Always `spoof` (index 1) — see spoof-only caveat above. |
| notes | string (JSON) | `{"utterance_id", "speaker_id", "emotion", "tts_model", "transcript"}`. |

## Quick Start

```python
from datasets import load_dataset
ds = load_dataset("SpeechAntiSpoofingBenchmarks/EmoSpoofTTS", split="test")
```

## Stats

| n_total | n_bonafide | n_spoof | total duration |
|---|---|---|---|
| 36000 | 0 | 36000 | ~29h 51m |

## Source provenance

- Source: `/mnt/datasets/emospoof/{StyleTTS2,F5TTS,CosyVoice}/<speaker>/<emotion>/*.wav`
  from the EmoSpoof-TTS v1.0 release (Mahapatra et al., 2025, JHU SMILE Lab).
- Transcript and emotion metadata recovered from
  `/mnt/datasets/emospoof/wav_list/<speaker>.txt`
  (`AUDIO_ID\tTRANSCRIPT\tEMOTION`), matched to each clip by stripping the
  trailing model suffix (`t1`=StyleTTS2, `t2`=F5-TTS, `t3`=CosyVoice) from the
  filename stem.
- All 36,000 clips are `label=spoof`; no bonafide audio is included (see
  spoof-only caveat above).

## Evaluation

See `eval.yaml` and `submissions/README.md`. This dataset is spoof-only and is
scored with `srr_complement` (1-SRR, lower is better) via a DeepVoice threshold
transfer; the legacy `eer_percent` is degenerate on this dataset alone and is
interpreted as a miss-rate / attack-difficulty probe rather than a
conventional EER.

## Citation

**Original paper**: Mahapatra, A., Ulgen, I.R., Naini, A.R., Busso, C., Sisman,
B. "Can Emotion Fool Anti-spoofing?" Interspeech 2025. arXiv:2505.23962.

```bibtex
@article{mahapatra2025emotion,
  title   = {Can Emotion Fool Anti-spoofing?},
  author  = {Mahapatra, Aurosweta and Ulgen, Ismail Rasim and Naini, Abinay Reddy and Busso, Carlos and Sisman, Berrak},
  journal = {arXiv preprint arXiv:2505.23962},
  year    = {2025}
}
```

## Maintainer

Maintained by Kirill Borodin (SpeechAntiSpoofingBenchmarks).
- Email: ~~k.n.borodin@mtuci.ru~~ (deprecated — use kborodin.research@gmail.com)
- Telegram: [@korallll_ai](https://t.me/korallll_ai)