Emotion clustering using the results of subjective opinion tests for emotion recognition in infants' cries

This paper proposes an emotion clustering procedure for emotion detection in infants’ cries. Our clustering procedure is performed using the results of subjective opinion tests regarding the emotions expressed in infants’ cries. Through the procedure, we obtain a tree data structure of emotion clusters that are generated by the progressive merging of emotions. Emotion merging is carried out on the condition that the objective function concerning the ambiguity of emotions that were detected in the opinion tests is minimized. Clustering experiments are performed on the results of opinion tests completed by infants’ mothers and baby-rearing experts. The experimental results show that the proposed clustering, which considers the evaluation rank of each emotion, is superior to the clustering that is only concerned with the detection/nondetection of each emotion. Based on the clustering results, we performed a preliminary recognition experiment on two emotion clusters. According to the recognition results, the proposed emotion cluster achieves a detection rate of 75%, which shows the effectiveness of the proposed clustering procedure.