Bioinspired dual-channel speech recognition using graphene-based electromyographic and mechanical sensors

Abstract

Automatic speech recognition (ASR) is helpful to improve quality of life. However, the performance of ASR degrades in the case of noisy environment, limited privacy, and speech disorders. Herein, we analyze the generation mechanism of speech and utilize dual-biological-channel for speech recognition. We also propose a dual-channel graphene-based electromyographic (EMG) and mechanical sensor (DGEMS) that can simultaneously collect two bio-signals at the same point. The EMG electrodes have better performance compared with commercial electrodes, and the mechanical sensors exhibit excellent repeatability during 10 million fatigue testing. Based on the excellent performance of EMG electrodes and mechanical sensors, 100% accuracy is achieved on digits dataset and 96.85% accuracy on a dataset containing 71 words. We also demonstrate that the DGEMS can be very resistant to noise, and the accuracy is always higher than 95%. Dual biological channels can greatly improve the performance of speech recognition in more scenarios. • A dual-channel graphene-based EMG and mechanical sensor for speech recognition • The graphene-based mechanical sensors exhibit excellent repeatability • An accuracy of 96.85% is achieved on a 71-word dataset • High speech recognition accuracy can be maintained in noisy environments In this work, Tian et al. propose a dual-channel graphene-based electromyographic and mechanical sensor for speech recognition with 100% accuracy based on digits dataset. This work highlights a promising solution to improve the performance of speech recognition in complex environments, such as noisy environment, limited privacy, and speech disorders.