Eardrum-inspired soft viscoelastic diaphragms for CNN-based speech recognition with audio visualization images

Abstract

Abstract In this study, we present initial efforts for a new speech recognition approach aimed at producing different input images for convolutional neural network (CNN)-based speech recognition. We explored the potential of the tympanic membrane (eardrum)-inspired viscoelastic membrane-type diaphragms to deliver audio visualization images using a cross-recurrence plot (CRP). These images were formed by the two phase-shifted vibration responses of viscoelastic diaphragms. We expect this technique to replace the fast Fourier transform (FFT) spectrum currently used for speech recognition. Herein, we report that the new creation method of color images enabled by combining two phase-shifted vibration responses of viscoelastic diaphragms with CRP shows a lower computation burden and a promising potential alternative way to STFT (conventional spectrogram) when the image resolution (pixel size) is below critical resolution.