<b>A Pilot Speech Corpus for Studying Device and Environmental Variability in Voice Biometrics</b>

This dataset provides a curated pilot corpus for studying <b>device and environmental variability in voice biometrics</b>. It contains <b>480 speech recordings</b> from <b>12 participants</b> (Japan, Nigeria, Ivory Coast, France, Germany, and Indonesia), each contributing 40 utterances recorded across multiple devices and environments.Recordings were made using the Samsung A04s, OnePlus Nord (both direct and in-call), iPhone 15 Pro, and a USB condenser microphone (connected to a <b>MacBook)</b>, under both <b>indoor (semi-controlled lobby)</b> and <b>outdoor (campus)</b> conditions. All files are stored in <b>WAV format (8–16 kHz, 16-bit PCM)</b>, accompanied by a <b>metadata file (CSV/Excel)</b> with anonymized attributes such as nationality, gender, age, and English proficiency.The dataset supports research in <b>speech enhancement</b> (spectral subtraction, Wiener filtering, adaptive filtering), <b>speaker identification and verification</b>, <b>spoofing resilience</b>, and <b>liveness detection</b>. Validation experiments confirmed that adaptive filtering achieved the highest accuracy (97%), highlighting both the challenges of cross-device variability and the potential for robust enhancement methods.This corpus provides a valuable benchmark for developing <b>secure and consistent voice biometric systems</b>, particularly in real-world applications such as <b>mobile banking authentication</b> and <b>low-resource environments</b>.