Datasets:
configs:
- config_name: default
data_files:
- split: train
path: audio/**/*.parquet
annotations_creators:
- expert-generated
- machine-generated
language_creators:
- found
- machine-generated
language:
- pt
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- extended|other-camara-deputados-audio
task_categories:
- audio-classification
task_ids:
- speaker-identification
paperswithcode_id: parlaspoof-br
pretty_name: ParlaSpoof-BR
tags:
- audio-deepfake-detection
- synthetic-voice-detection
- anti-spoofing
- voice-cloning
- speech-synthesis
- political-speech
- brazilian-portuguese
ParlaSpoof-BR: A Brazilian Portuguese Political Speech Audio Deepfake Dataset
ParlaSpoof-BR is the first audio deepfake detection benchmark specifically designed for political speech in Brazilian Portuguese. Constructed from official recordings of the Brazilian Chamber of Deputies, the dataset provides a domain-specific evaluation framework for electoral integrity and assesses detector robustness against modern zero-shot voice cloning, voice conversion, and semantically targeted partial manipulation (speech infilling).
📌 Key Features
- Authentic Bona Fide Utterances: 2,000 real speech samples from 40 parliamentarians (20 male, 20 female), balanced across all 5 geographic regions of Brazil (North, Northeast, Center-West, Southeast, and South).
- Full-Synthesis Attacks (TTS & VC):
- 5 Zero-Shot TTS Models: Chatterbox Multilingual V3, XTTS-v2, OmniVoice, VoxCPM2, and Qwen3-TTS.
- 5 Voice Conversion (VC) Models: Seed-VC, kNN-VC, OpenVoice-v2, X-VC, and EZ-VC.
- Partial Manipulation Attacks (Speech Infilling):
- Masked infilling via OmniVoice with word alignments provided by WhisperX.
- LLM-Guided Semantic Attacks: Meaning-inverting edits (antonyms, numbers, names, phrases, and negations) preserving acoustic context.
- Contiguous Resynthesis: Span resynthesis covering 25%, 50%, and 75% of the utterance duration.
- Acoustic Robustness Perturbations:
- Babble Noise Injection: Parliamentary background chatter added at 20 dB, 15 dB, and 10 dB SNR.
- Lossy Codec Compression: Roundtrip transcoding for MP3 and OGG formats.
- Speech Enhancement: Resemble Enhance, Demucs, and MetricGAN+.
- Total Scale: 134,400 audio files (11,200 bona fide / 123,200 spoofed).
📊 Dataset Composition
The benchmark is partitioned into a Core Evaluation Set (unperturbed primary set) and a Robustness Set (acoustic perturbations and codec variants).
| Label | Subset / Source | File Count |
|---|---|---|
| Bona fide | Original Chamber of Deputies recordings | 2,000 |
| Bona fide | Speech enhancement variants (Resemble Enhance, Demucs, MetricGAN+) | 6,000 |
| Bona fide | Transcoded variants (MP3 / OGG $\rightarrow$ WAV) | 3,200 |
| Spoof | Text-to-Speech (TTS) — 5 generators | 10,000 |
| Spoof | Voice Conversion (VC) — 5 systems | 10,000 |
| Spoof | Partial Manipulation (OmniVoice Infilling) | 8,000 |
| Spoof | Babble Noise Injection (SNRs: 10, 15, 20 dB) | 60,000 |
| Spoof | Transcoded variants (MP3 / OGG $\rightarrow$ WAV) | 35,200 |
| (Excluded) | Voice-cloning reference prompts ($\ge$ 10s per speaker) | 200 |
| Summary | Core Evaluation Set | 30,000 |
| Summary | Robustness Variants Set | 104,400 |
| TOTAL | Total Benchmark Files | 134,400 |
📈 Benchmark Detector Performance
We evaluated three state-of-the-art audio anti-spoofing architectures (AASIST, AASIST-L, and DF-Arena-1B). The empirical results reveal a severe cross-domain generalization gap when models trained on standard benchmarks (such as ASVspoof) are applied to real-world political speech in Brazilian Portuguese.
Overall Performance on Core Evaluation Set (30,000 files)
| Detector | EER (%) $\downarrow$ | AUC $\uparrow$ | Precision | Recall | Macro-F1 | Accuracy (%) |
|---|---|---|---|---|---|---|
| AASIST | 50.98 | 0.481 | 0.911 | 0.923 | 0.503 | 84.85 |
| AASIST-L | 53.70 | 0.445 | 0.911 | 0.962 | 0.500 | 87.99 |
| DF-Arena-1B | 32.30 | 0.715 | 0.944 | 0.830 | 0.595 | 80.05 |
Note: AASIST and AASIST-L exhibit severe bias, classifying over 90% of genuine parliamentary speech as spoofed (False Positive Rates of 91.7% and 95.4%).
DF-Arena-1B Performance by Synthesis Generator
| Attack Family | Generator / Method | EER (%) $\downarrow$ | AUC $\uparrow$ | Recall (%) $\uparrow$ |
|---|---|---|---|---|
| VC | OpenVoice-v2 | 19.6 | 0.892 | 99.7 |
| VC | kNN-VC | 21.4 | 0.849 | 99.4 |
| VC | X-VC | 23.2 | 0.820 | 98.1 |
| VC | Seed-VC | 30.0 | 0.743 | 94.0 |
| VC | EZ-VC | 36.0 | 0.671 | 83.1 |
| TTS | XTTS-v2 | 25.5 | 0.808 | 95.8 |
| TTS | Chatterbox Multilingual V3 | 26.1 | 0.799 | 96.5 |
| TTS | OmniVoice | 33.9 | 0.705 | 87.5 |
| TTS | VoxCPM2 | 53.1 | 0.463 | 41.4 |
| TTS | Qwen3-TTS | 58.0 | 0.390 | 31.2 |
🔍 Key Findings & Bias Analysis
- Methodological Bias Dominates Demographic Factors:
- Synthesis Generator Choice: Massive disparity in detectability (a 68.5 pp gap between OpenVoice-v2 and Qwen3-TTS). Portuguese-optimized TTS models (e.g., VoxCPM2, Qwen3-TTS) evade detection at rates exceeding 60%.
- Gender & Region: Demographic disparities are minimal in comparison (a 0.7 pp gap for gender and a 3.7 pp gap across regional accents).
- Efficacy of Partial Manipulation:
- Replacing a few consequential words is significantly more stealthy than full utterance synthesis. The detection rate (recall) for DF-Arena-1B drops to 29.2% on 25% modified audio (compared to 73.5% on 75% modified audio).
- Codec Asymmetry and Noise Vulnerability:
- Genuine audio transcoded via OGG causes a catastrophic 94.8% false positive rate in DF-Arena-1B, as compression artifacts closely mimic neural vocoder signatures.
- Adding authentic background chatter (babble noise at 10 dB SNR) allows 22.2% of previously detected deepfakes to successfully evade detection.
📁 File Structure & Metadata
Each sample in the dataset includes automated transcriptions, word-level alignments (WhisperX), and rich metadata annotations:
{
"id": "parlaspoof_br_001234",
"speaker_id": "dep_042",
"gender": "female",
"region": "Norte",
"label": "spoof",
"attack_category": "partial_manipulation",
"generator_model": "OmniVoice",
"infill_strategy": "LLM_semantic_antonym",
"modification_percentage": 0.15,
"transcription": "O projeto de lei foi rejeitado na comissão.",
"audio": {
"path": "audio/spoof/infill/parlaspoof_br_001234.wav",
"sampling_rate": 16000
}
}
📜 License and Ethical Use
The ParlaSpoof-BR dataset is released under the Creative Commons Attribution 4.0 International License (CC BY 4.0), inheriting the open redistribution license of the official Chamber of Deputies audio archive.
- Intended Use: Academic research, benchmarking audio forensics tools, supporting fact-checking initiatives, and bolstering democratic electoral integrity.
- Prohibited Use: Malicious impersonation, synthesis of political disinformation, or any activity that violates electoral integrity regulations is strictly forbidden.