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The way that humans encode their emotion into speech signals is complex. Forinstance, an angry man may increase his pitch and speaking rate, and useimpolite words. In this paper, we present a preliminary study on variousemotional factors and investigate how each of them impacts modern emotionrecognition systems. The key tool of our study is the SpeechFlow modelpresented recently, by which we are able to decompose speech signals intoseparate information factors (content, pitch, rhythm). Based on thisdecomposition, we carefully studied the performance of each informationcomponent and their combinations. We conducted the study on three differentspeech emotion corpora and chose an attention-based convolutional RNN as theemotion classifier. Our results show that rhythm is the most importantcomponent for emotional expression. Moreover, the cross-corpus results are verybad (even worse than guess), demonstrating that the present speech emotionrecognition model is rather weak. Interestingly, by removing one or severalunimportant components, the cross-corpus results can be improved. Thisdemonstrates the potential of the decomposition approach towards ageneralizable emotion recognition.

How Speech is Recognized to Be Emotional - A Study Based on Information Decomposition