Emotion recognition and school violence detection from children speech
School violence is a serious problem all over the world, and violence detection is significant to protect juveniles. School violence can be detected from the biological signals of victims, and emotion recognition is an important way to detect violence events. In this research, a violence simulation experiment was designed and performed for school violence detection system. Emotional voice from the experiment was extracted and analyzed. Consecutive elimination process (CEP) algorithm was proposed for emotion recognition in this paper. After parameters optimization, SVM was chosen as the classifier and the algorithm was validated by Berlin database which is an emotional speech database of adults, and the mean accuracy for seven emotions was 79.05%. The emotional speech database of children extracted in violence simulation was also classified by SVM classifier with proposed CEP algorithm, and the mean accuracy was 66.13%. The results showed that high classification performance could be achieved with the CEP algorithm. The classification result was also compared with database of adults, and the results indicated that children and adults’ voice should be treated differently in speech emotion recognition researches. The accuracy of children database is lower than adult database; the accuracy of violence detection will be improved by other signals in the system.