license: mit
language:
- en
pretty_name: VoxCelebSpoof
task_categories:
- audio-classification
- text-to-speech
tags:
- code
size_categories:
- 100K<n<1M
VoxCelebSpoof
VoxCelebSpoof is a dataset related to detecting spoofing attacks on automatic speaker verification systems. This dataset is part of a broader effort to improve the security of voice biometric systems against various types of spoofing attacks, such as replay attacks, voice synthesis, and voice conversion.
Dataset Details
Dataset Description
The VoxCelebSpoof dataset includes a range of audio samples from different types of synthesis spoofs. The goal of the dataset is to develop systems that can accurately distinguish between genuine and spoofed audio samples.
Key features and objectives of VoxCelebSpoof include:
Data Diversity: The dataset is derived from VoxCeleb, a large-scale speaker identification dataset containing celebrity interviews. Due to this, the spoofing detection models trained on VoxCelebSpoof are exposed to various accents, languages, and acoustic environments.
Synthetic Varieties: The spoofs include a variety of synthetic (TTS) attacks, such as high-quality synthetic speech, using AI-based voice cloning, and challenging systems to recognise and defend against a range of synthetic vulnerabilities.
Benchmarking: VoxCelebSpoof can serve as a benchmark for comparing the performance of different spoofing detection systems under standardised conditions.
Research and Development: The dataset encourages the research community to innovate in anti-spoofing for voice biometric systems, promoting advancements in techniques like feature extraction, classification algorithms, and deep learning.
Curated by: Matthew Boakes
Funded by: Bill & Melinda Gates Foundation
Shared by: Alan Turing Institute
Language(s) (NLP): English
License: MIT
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