Programmable Polymeric-Interface for Voiceprint Biometrics

Abstract

In the age of data science, voice data can be treated as one of the crucial assets used for strengthening biometrics technologies and biomedical applications. However, voice biometrics is still at emergence state facing voice variability, cross-linguistic variations, and voice spoofing challenges. These long-standing issues are proposed to be solved by deploying an organic, flexible, and printed piezoelectric polymeric interface exhibiting several unique features such as strong directionality, stability over a large operating temperature range (30–90 ˚C), broad frequency (6 kHz), ultra sensitivity (5.77 V Pa−1) and high signal-to-noise ratio (38–55 dB). By analyzing speech processing parameters acquired by the voice sensor, an individual voiceprint is established, which is further utilized to train a neural network model for deploying artificial intelligence (AI)-driven voice biometrics, resulting in a remarkable accuracy of >96% for population identification and speaker recognition and >93% for healthcare assessment. Featuring a versatile printing fabrication process, innovative voiceprint approach, and a robust neural network, the programmable polymeric acoustic interface proposes a promising complementary tool to the existing biometrics technologies and plays a vital role in healthcare monitoring.