Feature selection for Thai tone classification based on surface EMG

This paper aims to investigate the features of surface electromyography (sEMG) which can classify the Thai tonal sound for the EMG speech recognition and synthesis system. Signals were captured at seven positions on the strap muscles as a subject was uttering nine monosyllabic words which each of the words includes five tones. Eight features, i.e. Root Mean Square (RMS), Variance (VAR), Waveform Length (WL), Willson Amplitude (WAMP), Median Frequency (MDF) and three types of the Spectral Moment (SM), were computed and plotted on the scatter graph to cluster the tones. The results indicate that the EMG signal of the strap muscles can clearly classify the tones into three groups, i.e. a rising tone, a high tone, and the remainder clustered as one group. Moreover, RMS, VAR and WL can classify the high tone better than other features. All of the Spectral Moments yield similarly the results of classification, especially they can classify well the rising tone. For the remainder of the tones, while the scatter graphs are considered without the rising tone and the high tone, the low tone can be separated from their group when WL or WAMP is used for classification only.