--- title: "Real vs Fake Human Voice – Deepfake Audio Dataset" description: "Audio dataset containing 5,000 audio files featuring both genuine human recordings and AI-generated voice samples. Each set includes four speakers with multiple clips across M4A…" url: "https://unidata.pro/datasets/real-vs-fake-human-voice-deepfake-audio/" date_modified: "2025-12-01T13:36:20+03:00" language: "en-US" --- Audio dataset containing 5,000 audio files featuring both genuine human recordings and AI-generated voice samples. Each set includes four speakers with multiple clips across M4A and MP3 formats. The dataset supports research in deepfake detection, generated speech analysis, and real vs fake human voice recognition tasks. ## Dataset Structure ### The Numbers Section **Numbered list:** - **Number:** 5,000 — **Text:** Audio ### Tooltips Section **Tooltip items:** - **Name:** Speech Analysis - **Name:** ASR - **Name:** Machine learning - **Name:** Data generation - **Name:** Audio Processing ### Dataset Information **Table with data:** | Characteristic | Data | | --- | --- | | Description | Audio for deepfake voice detection, containing genuine human speech recordings paired with multiple matching synthetic copies. | | Data types | Audio | | Tasks | OCR, Computer Vision | | Total number of files | 5 000 | | Number of files in a set | 4 speakers × 5 clips × 4 audio files (1 original + 3 synthetic) | | Labeling | Metadata (country, gender, ID, age, audio group, audio name, audio text) | | Gender | Male, Female | **Media Slider:** - **Video on Slayder:** - **Video on Slayder:** **Link to the sample:** [Download sample](https://drive.google.com/drive/folders/1_NA7GscdXj0SB_Q4PWERrGv7afdSLXNw) ### Statistics - Charts **Charts with Titles:** - **Shortcode:** [ays_chart id='65'] — **caption above the graph:** Distribution by gender ### Technical Specifications **Table with data:** | Characteristic | Data | | --- | --- | | Audio Extensions | M4a, MP3 | | Data Type | generated | **Source and data collection methodology:** Source and collection methodology: Data was AI-generated. ### Dataset Use Cases - Slider **Industry Cards:** - **Industry:** Cybersecurity and Fraud Prevention — **Title:** Developing Reliable Deepfake Voice Detection Systems — **Text:** This deepfake audio dataset provides real human and AI-generated speech essential for detecting fake voices used in fraud and impersonation. With detailed metadata on gender, accent, and duration, it enables the training of accurate detection models. Applications include secure banking, telecommunication verification, and government systems, improving data protection and digital security against voice-based attacks. - **Industry:** Voice Authentication and Biometrics — **Title:** Improving Voice-Based Identity Verification — **Text:** The dataset supports voice recognition and biometric authentication by including both genuine and synthetic speech. Developers can train systems to detect spoofing, verify liveness, and enhance accuracy in voice-controlled platforms. Metadata on speaker identity and speech characteristics ensures models can differentiate real and fake voices, strengthening security in enterprise access, mobile authentication, and identity verification services. - **Industry:** Media Integrity and Journalism — **Title:** Detecting Synthetic Voices in Broadcasts and Online Content — **Text:** Researchers and forensic analysts can use this dataset to identify deepfake voices in media and social platforms. By providing real and AI-generated recordings, it enables training of models to detect manipulation, verify authenticity, and combat misinformation. Applications include journalism verification, monitoring social media, and supporting legal investigations in voice-based deception cases. - **Industry:** AI Ethics and Research — **Title:** Exploring Responsible Use of Synthetic Voice Technology — **Text:** This dataset enables research on ethical synthetic voice applications by comparing real and AI-generated speech. Experts can analyze imitation quality, emotional tone, and human-likeness to guide responsible AI use. Applications include improving accessibility, assistive technologies, and entertainment, while ensuring deepfake detection, ethical voice synthesis, and safe deployment in media and AI systems. ### Fact **FAQs Heading:** FAQs **List of Questions:** - **Question:** What types of annotations are provided? — **Answer:** Each file includes metadata annotations such as speaker ID, gender, accent, native locale, text ID, and duration in seconds. These labels help researchers track speaker variations, analyze speech patterns, and improve deepfake voice classification accuracy. - **Question:** How was the data collected? — **Answer:** The real human voice recordings were collected through crowdsourcing platforms under consented conditions. The synthetic speech samples were generated using AI-based TTS models, ensuring controlled and reproducible comparisons between authentic and generated voices. - **Question:** Can I request a sample of the dataset before purchasing or downloading it? — **Answer:** Yes. Unidata provides free sample files so you can evaluate audio quality, metadata accuracy, and synthetic generation consistency before purchase. These samples help you determine whether the dataset fits your machine learning or voice recognition project. - **Question:** How are Unidata datasets licensed? — **Answer:** Unidata datasets follow a dual-licensing model. Free samples are available for initial testing, while the complete dataset is available for purchase to ensure full access to all files and metadata. - **Question:** Do Unidata datasets follow GDPR or other data privacy regulations? — **Answer:** Yes. All Unidata datasets are created and distributed in compliance with GDPR and international data protection laws. Every voice recording and generated sample is handled ethically, ensuring no personal or identifiable data is included. - **Question:** How are Unidata datasets stored? — **Answer:** All datasets are securely stored on AWS cloud infrastructure, providing high availability, data integrity, and scalability. Unidata’s storage practices comply with ISO 27001 and ISO 27701 standards, ensuring a secure and privacy-focused environment for handling audio data. - **Question:** Is this a real-world dataset or synthetic data? — **Answer:** The dataset includes both real-world human voice recordings and synthetic deepfake audio. This combination provides a balanced foundation for training AI models to differentiate between authentic and generated speech, enhancing the performance of deepfake detection systems. - **Question:** Why are real and fake voice samples paired in this dataset? — **Answer:** Paired real and synthetic recordings allow researchers to directly compare authentic speech characteristics with AI-generated variations. This structure helps train more accurate voice detection models by providing clear examples of both legitimate and manipulated audio. [Full list of this site's AI-readable pages](https://unidata.pro/llms.txt)