SEMG Approach for Speech Recogition

Abstract

Speech is the most familiar and habitual way of communication used by most of us. Due to speech disabilities, many people find it difficult to properly voice their views and thus are at a disadvantage. The research tackles the issue of lack of speech from a speech impaired user by recognizing it with the use of ML models such as Gaussian Mixture Model - GMM and Convolutional Neural Network - CNN. With properly recorded and cleaned muscle activity from the facial muscles it is possible to predict the words being uttered/whispered with a certain accuracy. The intended system will additionally also have a visual aid system which can provide better accuracy when used together with the facial muscle activity-based system. Neuromuscular signals from the speech articulating muscles are recorded using Surface ElectroMyoGraphy (SEMG) sensors, which will be used to train the machine learning models. In this paper we have demonstrated various signals synthesized through the ElectroMyography system and how they can be classified using machine learning models such as Gaussian Mixture Model and Convolutional Neural Network for the visual-based lip-reading system.