---
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

[![License: CC BY 4.0](https://img.shields.io/badge/License-CC_BY_4.0-blue.svg)](https://creativecommons.org/licenses/by/4.0/)
[![Dataset on Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Dataset-yellow)](https://huggingface.co/datasets/freds0/ParlaSpoof-BR)
[![Project Page](https://img.shields.io/badge/Website-Demo%20%26%20Info-blue)](https://ermisai.github.io/parlaspoof-br-demo)

**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

1. **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).
2. **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).
3. **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:

```json
{
  "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.

---