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Emotion shapes all aspects of our interpersonal and intellectual experiences.Its automatic analysis has there-fore many applications, e.g., human-machineinterface. In this paper, we propose an emotional tonal speech dataset, namelyMandarin Chinese Emotional Speech Dataset - Portrayed (MES-P), with both distaland proximal labels. In contrast with state of the art emotional speechdatasets which are only focused on perceived emotions, the proposed MES-Pdataset includes not only perceived emotions with their proximal labels butalso intended emotions with distal labels, thereby making it possible to studyhuman emotional intelligence, i.e. people emotion expression ability and theirskill of understanding emotions, thus explicitly accounting for perceptiondifferences between intended and perceived emotions in speech signals andenabling studies of emotional misunderstandings which often occur in real life.Furthermore, the proposed MES-P dataset also captures a main feature of tonallanguages, i.e., tonal variations, and provides recorded emotional speechsamples whose tonal variations match the tonal distribution in real lifeMandarin Chinese. Besides, the proposed MES-P dataset features emotionintensity variations as well, and includes both moderate and intense versionsof recordings for joy, anger, and sadness in addition to neutral speech.Ratings of the collected speech samples are made in valence-arousal spacethrough continuous coordinate locations, resulting in an emotional distributionpattern in 2D VA space. The consistency between the speakers' emotionalintentions and the listeners' perceptions is also studied using Cohen's Kappacoefficients. Finally, we also carry out extensive experiments using a baselineon MES-P for automatic emotion recognition and compare the results with humanemotion intelligence.

MES-P: an Emotional Tonal Speech Dataset in Mandarin Chinese with Distal and Proximal Labels