Improvement of emotion recognition by Bayesian classifier using non-zero-pitch concept

Emotion recognition is an important factor in the development of human-robot interactions (HRI) and especially, the pitch information is considered for features related to emotion in speech emotion recognition. Thus, in this paper, the goal is to propose the improved method to recognize the emotion by pitch, "non-zero-pitch", that is, the pitch contour does not have the zero value. We have applied this concept to a Bayesian classifier, and obtained better results for emotion recognition than those attained using the previous pitch contour. In this study, we explain precisely the concept of "non-zero-pitch" and show its superiority over the previous pitch concept. Moreover, it is also important to determine emotions to be classified. In general, many researchers of emotion recognition use the classification of primary emotions such as anger, joy, and so on. However, they differ on the number and kind of primary emotions to use and generally fail to explain the rationale for their classification. Psychologists have also debated the topic of primary emotions. In the present study we propose a classification method of primary emotions for the field of HRI.