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README.md

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+# AffectDF: Emotionally Expressive Speech Deepfake Benchmark
+
+## Overview
+
+AffectDF is a large-scale benchmark for speech deepfake detection under emotionally expressive spoofing conditions. The dataset is designed to evaluate whether current speech deepfake detection (SDD) systems can generalize beyond conventional neutral-speech benchmarks to modern emotional and expressive speech attacks.
+
+AffectDF contains approximately 260 hours of audio generated using 21 spoofing attacks across five emotional states. The dataset includes both acted emotional speech and spontaneous emotional speech, enabling analysis of robustness across different emotional speaking styles.
+
+The dataset is organized into three official splits:
+
+* Train/
+* Dev/
+* Test/
+
+Protocol files are provided for reproducible evaluation.
+
 ---
-license: cc-by-4.0
+
+## Source Corpora
+
+AffectDF is constructed using bona fide emotional speech from two source corpora:
+
+* ESD (Emotional Speech Dataset): acted emotional speech
+* MSP-Podcast: spontaneous emotional speech
+
+The use of both corpora enables evaluation across controlled acted emotional speech and naturalistic spontaneous emotional speech.
+
 ---
+
+## Emotion Categories
+
+AffectDF covers five emotional states:
+
+* Angry
+* Happy
+* Neutral
+* Sad
+* Surprise
+
+---
+
+## Dataset Design
+
+AffectDF contains both bona fide and synthetic emotional speech samples.
+
+* Bona fide samples are taken from ESD and MSP-Podcast.
+* Synthetic samples are generated using diverse spoofing systems spanning TTS, VC, EVC, and LALM-based attacks.
+* The dataset supports analysis across emotion labels, attack types, generation models, speakers, and acted versus spontaneous emotional speech.
+* Many samples follow a parallel or controlled design, where synthetic samples can be aligned with corresponding source utterances using audio IDs and protocol metadata.
+
+---
+
+## Spoofing Attack Types
+
+AffectDF includes attacks from multiple generation paradigms:
+
+* TTS: text-to-speech attacks
+* VC: voice conversion attacks
+* EVC: emotional voice conversion attacks
+* VC+EVC: combined voice and emotional voice conversion attacks
+* LALM-EVC: large audio-language model based emotional voice conversion attacks
+
+The benchmark includes both conventional generation systems and recent large audio-language model based attacks.
+
+---
+
+## Directory Structure
+
+The dataset is organized as follows:
+
+
+AffectDF/
+├── Train/
+├── Dev/
+├── Test/
+└── Protocols/
+    ├── train_protocol.txt
+    ├── dev_protocol.txt
+    └── test_protocol.txt
+
+
+Example audio filenames:
+
+
+R_0015_000172.wav
+CV_0016_001189.wav
+A2S_0015_0015_000654_Angry_to_Sad.wav
+MCPM_A2N_0017_0017_000613_Angry_to_Neutral.wav
+StA_N2A_0015_000197.wav
+
+
+---
+
+## Protocol Files
+
+The Protocols/ directory contains split-specific metadata files:
+
+
+Protocols/train_protocol.txt
+Protocols/dev_protocol.txt
+Protocols/test_protocol.txt
+
+
+Each protocol entry contains metadata needed for evaluation and analysis.
+
+Example entry:
+
+0011 A2A_0011_0011_000351_Angry_to_Angry A01 Angry EVC Qwen2.5-Omni spoof
+
+
+The fields include:
+
+
+speaker_id audio_id attack_id emotion generation_type generation_model label
+
+
+Where:
+
+* speaker_id: speaker identifier
+* audio_id: utterance or file identifier
+* attack_id: attack system identifier
+* emotion: emotion label
+* generation_type: attack category, such as TTS, VC, EVC, or LALM-EVC
+* generation_model: model used to generate the sample
+* label: real or spoof
+
+Users should rely on these protocol files for split membership, labels, attack IDs, and metadata parsing.
+
+---
+
+## Bona Fide and Synthetic Pairing
+
+AffectDF supports controlled comparison between bona fide and synthetic speech. Synthetic samples can be linked to corresponding or related bona fide samples through shared audio ID components in the filenames and protocol entries.
+
+This design enables:
+
+* real-versus-spoof comparison
+* emotion-wise analysis
+* attack-wise analysis
+* acted versus spontaneous comparison
+* controlled evaluation across generation methods
+
+---
+
+## Intended Use
+
+AffectDF is intended for research on:
+
+* speech deepfake detection
+* emotional speech deepfake detection
+* spoofing countermeasures
+* robustness to emotionally expressive attacks
+* cross-domain SDD generalization
+* attack-wise and emotion-wise SDD analysis
+* acted versus spontaneous emotional speech robustness
+* parallel bona fide/synthetic speech analysis
+
+The dataset is intended for research and non-commercial use only.
+
+---
+
+## Ethical Use
+
+AffectDF is released to support research on robust and responsible speech deepfake detection. The dataset should not be used for impersonation, deception, biometric spoofing, fraud, misinformation, or any harmful application.
+
+Users should respect the licenses and terms of the original source corpora and generation models used to construct the dataset.
+
+---
+
+---
+
+## Data License and Intended Use
+
+Data License and Intended Use. AffectDF is released for research and non-commercial use only under a CC BY-NC 4.0 license. The release includes generated audio and protocol files, subject to the licenses and terms of the original source corpora and generation models. The dataset is intended only for speech deepfake detection research and must not be used for impersonation, deception, biometric spoofing, or other harmful applications.
+---
+
+## Notes
+
+* AffectDF contains both real and generated emotional speech.
+* The dataset includes acted and spontaneous emotional speech.
+* The test split includes unseen attacks and spontaneous emotional speech conditions.
+* The protocol files should be used as the authoritative source for metadata and labels.
+* Emotion labels are provided to support fine-grained robustness analysis beyond binary real-versus-spoof detection.