ABSTRACT

This paper determines the angry emotion condition by analyzing and recognizing speech signal, EEG signal, as well as detecting the heartbeat. For the speech analyzing experiment, several digital signal processing methods such as autocorrelation and linear predication technique was introduced to analyze the features. Then, Artificial Neural Network (ANN) was used to classify each parameter features such as mean fundamental frequency, maximum fundamental frequency, standard deviation fundamental frequency, mean amplitude, pause length ratio and first formant frequency to recognize the emotion. For the EEG analysis, the raw EEG signal was undergone preprocessing to remove the artifacts to minimal. Some features as mean, standard deviation, the peak amplitude, the peak amplitude in alpha band (PAA) and the peak frequency in alpha band (PAF) of the EEG signals were extracted. The selected features were classified by using ANN to obtain the maximum classification accuracy rate. Meanwhile, a heartbeat monitoring circuit was developed to measure the heartbeat. The result showed that angry emotion has relatively low condition in mean value, maximum peak amplitude and relatively high peak frequency in alpha band (PAF) of the EEG signals. The mean fundamental frequency, standard deviation fundamental frequency and mean intensity of the speech signal are good in determining the angry emotion. This method can be used further to recognize angry emotion of patient during counseling session.