Cross-Cultural Emotion Recognition and Comparison Using Convolutional Neural Networks


  • Alexander Iliev SRH University Berlin, Charlottenburg, Germany; Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, Sofia, Bulgaria
  • Ameya Mote SRH University Berlin, Charlottenburg, Germany
  • Arjun Manoharan SRH University Berlin, Charlottenburg, Germany



Emotion Recognition, Speech Analysis, Language Processing, Convolutional Neural Networks, Cultural Comparison


The paper sets to define a comparison of emotions across 3 different cultures namely Canadian French, Italian, and North American. This was achieved using speech samples for each of the three languages subject to our study. The features used were MFCCs and were passed through convolutional neural network in order to verify their significance for the task of emotion recognition through speech. Three different systems were trained and tested, one for each language. The accuracy came to 71.10%, 79.07%, and 73.89% for each of them respectively. The aim was to prove that the feature vectors we used were representing each emotion well. A comparison across each emotion, gender and language was drawn at the end and it was observed that apart from the emotion neutral , every other emotion was expressed somewhat differently by each culture. Speech is one of the main vehicles to recognize emotions and is an attractive area to be studied with application to presenting and preserving different cultural and scientific heritage.


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How to Cite

Iliev, A., Mote, A., & Manoharan, A. (2020). Cross-Cultural Emotion Recognition and Comparison Using Convolutional Neural Networks. Digital Presentation and Preservation of Cultural and Scientific Heritage, 10, 89–102.