Deep Learning‐Enabled MXene/PEDOT:PSS Acoustic Sensor for Speech Recognition and Skin‐Vibration Detection

Abstract

Flexible acoustic sensors with high sensitivity, excellent mechanical strength, and easy integration are urgently needed for wearable electronics. MXene holds great promise as a sensing material for this application. However, low flexibility and stability limit the performance of MXene-based composites. To alleviate the aforementioned issue, a flexible pressure sensor based on MXene/poly(3,4-ethylenediox-ythiophene)-poly(styrenesulfonate) (PEDOT:PSS) is fabricated and used as an acoustic sensor inhibiting high sensitivity, fast response time (57 ms), ultra-thin thickness (30 mu m), and remarkable stability. Excellent performance enables the sensor to detect and identify weak muscle movements and skin vibrations, such as word pronunciation and carotid artery pulse. Furthermore, by combining the proposed deep learning model based on number recognition convolutional neural network (NR-CNN), speech recognition toward different pronunciations of numbers that appear frequently in daily conversations can be realized. High recognition accuracy (91%) is achieved by training and testing the proposed NR-CNN with large amounts of data recorded by the sensor. Results demonstrate that the flexible and wearable MXene/PEDOT:PSS acoustic sensor accelerates intelligent artificial acoustics and possesses great potential for applications involving speech recognition and health monitoring.