Parallel-Inception CNN Approach for Facial sEMG based Silent Speech Recognition

With the purpose of providing an external human-machine interaction platform for the elderly in need, a novel facial surface electromyography based silent speech recognition system was developed. In this study, we propose a deep learning architecture named Parallel-Inception Convolutional Neural Network (PICNN), and employ up-to-date feature extraction method log Mel frequency spectral coefficients (MFSC). To better meet the requirements of our target users, a 100-class dataset containing daily life-related demands was designed and generated for the comparative experiments. According to experimental results, the highest recognition accuracy of 88.44% was achieved by proposed recognition framework based on MFSC and PICNN, exceeding the performance of state-of-the-art deep learning algorithms such as CNN, VGGNet and Inception CNN (3.22%, 4.09% and 1.19%, respectively). These findings suggest that the newly developed silent speech approach holds promise to provide a more reliable communication channel, and the application scenery of speech recognition technology has been expanded at the same time.