Japanese Vowels Recognition Using Linear Discriminant Analysis and Surface Electromyogram Measured with Bipolar Dry Type Sensors
Ryosuke Takabatake, Shin-ichi Ito, Momoyo Ito, Minoru Fukumi · 2017
This paper proposes a Japanese vowels recognition method using surface electromyogram (EMG). First, 3 sensors are used to measure surface EMG data at orbicularis oris muscle, zygomatic muscle and depressor angle oris muscle. Next, Fast Fourier Transform (FFT) is applied to all measurement data to calculate power spectra. Linear Discriminant Analysis (LDA) is then used for power spectra of 3 channels and reduce their dimension to 4. Finally, the result of LDA is recognized by Support Vector Machine (SVM). In experiments, it is assumed that mounting sensors to face, measuring EMG, and demounting them are 1 trial. A subject utters 5 Japanese vowels 3 times. Among 3 trials data, 2 trials data are used to make templates and the remaining are used for test. The subject is a man in twenties. As a result, we obtained 62.3% average recognition accuracy. This result shows the proposed method is better about 2 times than the previous method.