📝 SummarXiv

Abstract

This paper introduces the Extended Length Audio Dataset for Synthetic Voice Detection and Speaker Recognition (ELAD SVDSR), a resource specifically designed to facilitate the creation of high quality deepfakes and support the development of detection systems trained against them. The dataset comprises 45 minute audio recordings from 36 participants, each reading various newspaper articles recorded under controlled conditions and captured via five microphones of differing quality. By focusing on extended duration audio, ELAD SVDSR captures a richer range of speech attributes such as pitch contours, intonation patterns, and nuanced delivery enabling models to generate more realistic and coherent synthetic voices. In turn, this approach allows for the creation of robust deepfakes that can serve as challenging examples in datasets used to train and evaluate synthetic voice detection methods. As part of this effort, 20 deepfake voices have already been created and added to the dataset to showcase its potential. Anonymized metadata accompanies the dataset on speaker demographics. ELAD SVDSR is expected to spur significant advancements in audio forensics, biometric security, and voice authentication systems.