Gradient-distributed metal-halide dynamic memristors for adaptive and robust voiceprint recognition

Inspired by the auditory system’s capacity to process spatiotemporal sound patterns, voiceprint recognition plays a vital role in identity authentication and security. However, current platforms often face challenges of speech frequency and amplitude variability, hindering accurate feature extraction in noisy environments. To address these issues, a large-scale hybrid metal-halide dynamic memristor (MHDM) featuring an engineered gradient-distributed architecture is developed for adaptive voiceprint recognition. The spontaneously graded metal-halide functional layer allows for precise modulation of Schottky barriers and redistribution of interface charges. This design achieves µs-scale response, enhances noise tolerance (over 20% improvement in signal-to-noise ratio), and enables kHz-scale dynamic signal processing. Experimental results demonstrate that the MHDM achieves a voiceprint recognition accuracy of 99.3%, maintaining high performance at 93.2% even in realistic background noise. These findings demonstrate the system’s potential for secure and efficient voiceprint recognition, combining scalability with robust performance in noisy environments. Voiceprint recognition is critical for biometric security, yet it remains susceptible to real-world acoustic disruption. Shao et al. report a gradient-distributed metal-halide memristor that exploits intrinsic adaptive dynamics to achieve robust and accurate voice recognition under noisy conditions.