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

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.

@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