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The fact that emotions play a vital role in social interactions, along withthe demand for novel human-computer interaction applications, have led to thedevelopment of a number of automatic emotion classification systems. However,it is still debatable whether the performance of such systems can compare withhuman coders. To address this issue, in this study, we present a comprehensivecomparison in a speech-based emotion classification task between 138 AmazonMechanical Turk workers (Turkers) and a state-of-the-art automatic computersystem. The comparison includes classifying speech utterances into six emotions(happy, neutral, sad, anger, disgust and fear), into three arousal classes(active, passive, and neutral), and into three valence classes (positive,negative, and neutral). The results show that the computer system outperformsthe naive Turkers in almost all cases. Furthermore, the computer system canincrease the classification accuracy by rejecting to classify utterances forwhich it is not confident, while the Turkers do not show a significantly higherclassification accuracy on their confident utterances versus unconfident ones.

Emotion Classification: How Does an Automated System Compare to Naive Human Coders?