Authors:
Kifekachukwu Nwosu, Chloe Evered, Zahra Khanjani, Noshaba Bhalli, Lavon Davis, Christine Mallinson, Vandana P. Janeja
Where published:
AAAI Fall Symposium Series (FSS-23) by University of Maryland, Baltimore County; Rochester Institute of Technology; Georgetown University.
Dataset names (used for):
- The dataset includes 20 Text to Speech, 20 Voice Conversion, and 10 genuine clips, utilized to test the auto-annotation methodology.
The study focuses on detecting audio deepfakes through linguistic analysis. It involves analyzing audio samples for specific linguistic features and testing the auto-annotation methodology.
The dataset comprises 50 audio clips, including 20 Text to Speech, 20 Voice Conversion, and 10 genuine clips. It includes 344 audio samples with five main features: pitch, pause, breath, consonant bursts, and audio quality.
Keywords:
Audio Deepfake Detection, Linguistic Features, Time Series Discords, Expert Annotations
Instance Represent:
Audio samples analyzed for linguistic features
Dataset Characteristics:
Includes 344 audio samples with linguistic features such as pitch, pause, breath, consonant bursts, and audio quality.
Subject Area:
Audio security, linguistics, deepfake detection
Associated Tools:
Detection of audio deepfakes through linguistic analysis
Feature Type:
Audio and linguistic features
License: The paper is accessible through the conference series’ platform, with no additional license information provided.
Last Accessed: 6/14/2024