  Emotion shapes all aspects of our interpersonal and intellectual experiences.
Its automatic analysis has there-fore many applications, e.g., human-machine
interface. In this paper, we propose an emotional tonal speech dataset, namely
Mandarin Chinese Emotional Speech Dataset - Portrayed (MES-P), with both distal
and proximal labels. In contrast with state of the art emotional speech
datasets which are only focused on perceived emotions, the proposed MES-P
dataset includes not only perceived emotions with their proximal labels but
also intended emotions with distal labels, thereby making it possible to study
human emotional intelligence, i.e. people emotion expression ability and their
skill of understanding emotions, thus explicitly accounting for perception
differences between intended and perceived emotions in speech signals and
enabling studies of emotional misunderstandings which often occur in real life.
Furthermore, the proposed MES-P dataset also captures a main feature of tonal
languages, i.e., tonal variations, and provides recorded emotional speech
samples whose tonal variations match the tonal distribution in real life
Mandarin Chinese. Besides, the proposed MES-P dataset features emotion
intensity variations as well, and includes both moderate and intense versions
of recordings for joy, anger, and sadness in addition to neutral speech.
Ratings of the collected speech samples are made in valence-arousal space
through continuous coordinate locations, resulting in an emotional distribution
pattern in 2D VA space. The consistency between the speakers' emotional
intentions and the listeners' perceptions is also studied using Cohen's Kappa
coefficients. Finally, we also carry out extensive experiments using a baseline
on MES-P for automatic emotion recognition and compare the results with human
emotion intelligence.
