A Fusion of EMG and IMU for an Augmentative Speech Detection and Recognition System

Subvocal voice recognition is important in rehabilitation because it allows people with speech issues to communicate in an alternate manner. Capturing and interpreting subtle muscular movements during subvocalization can improve the rehabilitation process, allowing patients to have more autonomy and a higher quality of life. The scarcity of research on subvocal voice recognition, as well as the use of IMU (accelerometer and gyroscope) signals for speech activity detection, are significant challenges. The study focuses on the amalgamation of spectrotemporal feature extraction techniques for classification and IMU data for speech activity detection. The study carries out the classification of an isolated word vocabulary sets of 70 words corresponding to 6 subjects and 96 isolated words for subjects. A feature extraction algorithm was used utilizing Variational Mode Decomposition. The results demonstrated maximum accuracy rates of 98.6% for the 70-word set and 92% for the 96-word set. Automatic speech activity detection (ADSAS) was developed using only IMU data and was independent of the EMG data. The comparison was carried out by using a previously proposed EMG activity detection method based on Teager Kaiser Operator and morphological operations, The IMU based detection technique achieved a lowest error of 0.09 compared to 0.21 for the EMG based detection technique. The results proved that IMU based methods perform better than EMG based methods for detection of speech activity. Statistical analysis was carried out using paired t-test and resulted in a significant difference between the two techniques (p-value< 0.05).