Descriptor: Extended-Length Audio Dataset for Synthetic Voice Detection and Speaker Recognition (ELAD-SVDSR)

This paper presents the Extended-Length Audio Dataset for Synthetic Voice Detection and Speaker Recognition (ELAD-SVDSR), a comprehensive resource designed to advance both the generation of high-fidelity synthetic voices and the development of robust detection systems. Unlike existing datasets that typically provide short utterances, single-microphone recordings, or lack synthetic samples, ELAD-SVDSR uniquely integrates three critical features: 45-minute audio recordings from 36 participants, each reading various newspaper articles recorded under controlled conditions and captured via five microphones of differing quality, along with included deepfake voices for direct comparison. By having an extended duration of audio, ELAD-SVDSR captures a richer range of speech attributes such as pitch contours, intonation patterns, and nuanced delivery. This enables models to generate more realistic and coherent synthetic voices. The controlled microphone diversity enables systematic cross-device analysis, while the integrated synthetic samples eliminate the need for independent deepfake generation prior to detection research. Comparative evaluation demonstrates that the extended-duration training data may yield higher-quality deepfakes than those generated from existing datasets. Anonymized metadata accompanies the dataset to provide essential demographic information while preserving participant privacy. This dataset is anticipated to advance research in forensics, synthetic audio detection, biometric security, and voice authentication systems.