Feature Parameters extraction and Affective Computing of Voice Message for Social Media Environment

Peng Jiang1, Cui Guo2 and Yonghui Dai3

  1. Jingan Branch Campus, Shanghai Open University,
    Shanghai 200040, China
  2. Shanghai Lifelong Education School Credit Bank Management Center,
    Shanghai 200092, China
  3. Management School, Shanghai University of International Business and Economics,
    Shanghai 201620, China


Voice message in social media environment includes a large number of conversation natural languages, which increases the difficulty of emotion tagging and affective computing. In order to solve the above difficulties, this paper analyzes the cognitive differences between the semantic and acoustic features of voice message from the perspective of cognitive neuroscience, and presents a voice feature extraction method based on EEG (Electroencephalogram) experiments, and gets the representation of 25 acoustic feature parameter vectors. Meanwhile, we proposed an affective computing method based on PAD (Pleasure-Arousal-Dominance) dimension emotional space according to the above parameters. Experiments show that the method can effectively solve the affective computing problem of voice message. Overall, there are two main contributions of this paper. Firstly, it comprehensively analyzes the emotional cognitive feature of voice message in social media environment from the perspectives of cognitive neural mechanism, voice acoustic feature and text semantics. Secondly, the segmented affective computing method for voice message based on acoustic feature parameters and PAD emotional state model is proposed.

Key words

affective feature parameters, cognitive neuroscience, voice acoustic feature, emotion recognition

Digital Object Identifier (DOI)


Publication information

Volume 21, Issue 1 (January 2024)
Year of Publication: 2024
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Jiang, P., Guo, C., Dai, Y.: Feature Parameters extraction and Affective Computing of Voice Message for Social Media Environment. Computer Science and Information Systems, Vol. 21, No. 1, 57–74. (2024), https://doi.org/10.2298/CSIS230509066J