A Pattern Analysis of Using Self-Organizing-Maps in a Unspoken Vowel Recognition System Based on Surface Electromyogram
In this paper, we present some results of analysis on surface electromyogram (SEMG) using Self–Organizing–Maps (SOM) algorithm, which is one of neural networks algorithm, for unspoken vowel recognition system. Speech is the most natural way of communication with humans. However, the normal audible speech can not be used by speech handicapped people, for example laryngectomy surgeries or resection of vocal cords. In addition, audible speech can be extremely disturbing to others, for example in libraries, theaters, during meetings and so on. To overcome the limitation, unvoiced communication, which recognizes and synthesize speech by measured of SEMG from a face, has been investigated. Figure 1 shows the proposed unspoken vowel recognition system based on SEMG. Three pairs of Ag-AgCl electrodes were placed on a face and SEMG signals from Orbicularis oris muscle (CH1), Temporal fascia (CH2), and Sternohyoid muscle (CH3) were recorded by one frame was 510ms at the sampling rate of 1kHz. Figure 2 shows examples of SEMG signals of each channel during speaking Japanese vowels from /a/ to /o/. The moving RMS value of the each recorded signal was calculated using moving window of 50 samples over the whole signal. Therefore, max value of 10 coefficients were extracted