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An Effective Deep Learning Approach for Dialogue Emotion Recognition in Car-Hailing Platform

With the rapid development of Uber and Didi, car-hailing services have had significant societal impacts. However, the increased popularity of car-hailing has also created drawbacks, as its security is significant problem. Researchers try to recognize human emotions effectively to protect the passenger's personal safety. Most of the current approaches are intrusive to passengers, e.g. using camera to capture passenger's facial expression or identifying conversation between passenger and driver. In this paper, we study the intonation and speed of speech signal to recognize three emotions, which are 'Neutrality', 'Happiness' and 'Anger'. Our proposed method contains two stages to make classification. In stage one, we distinguish between passenger and driver using unsupervised spectral clustering method. Experimental results show that the accuracy of our method is superior to representative off-the-shelf methods. In stage two, we propose a CNNs-based emotion recognition method. The results demonstrate that the model has the ability to effectively recognize different emotions, which the accuracy attains 78.14%