Machine learning-based self-powered acoustic sensor for speaker recognition

Abstract

Herein, we report a new platform of machine learning-based speaker recognition via the flexible piezoelectric acoustic sensor (f-PAS) with a highly sensitive multi-resonant frequency band. The resonant self-powered f-PAS was fabricated by mimicking the operating mechanism of the basilar membrane in the human cochlear. The f-PAS acquired abundant voice information from the multi-channel sound inputs. The standard TIDIGITS dataset were recorded by the f-PAS and converted to frequency components by using a Fast Fourier Transform (FFT) and a Short-Time Fourier Transform (STFT). The machine learning based Gaussian Mixture Model (GMM) was designed by utilizing the most highest and second highest sensitivity data among multi-channel outputs, exhibiting outstanding speaker recognition rate of 97.5% with error rate reduction of 75% compared to that of the reference MEMS microphone.