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A Distributed Ensemble Machine Learning Technique for Emotion Classification from Vocal Cues

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Big Data Analytics (BDA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13773))

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Abstract

Human-computer interaction and the creation of humanoid robots both depend heavily on emotions. By integrating the concept of emotion understanding, intelligent software systems become more effective and intuitive in resembling human-human interactions. Typically, we combine factors like intonation (speech), facial expression (visual modality), and word content (text). All possible multimodal combinations must be taken into consideration to process emotions appropriately. Among multimodal approaches, the use of human audio samples for emotion processing is given more weight than the use of facial expressions. To accomplish accurate categorization, analyzing massive volumes of real-time data has become more necessary. Machine Learning (ML) models that operate in a distributed fashion are crucial, given the size and complexity of the problem under study. In this respect, we propose a distributed ensemble model for vocal cue-based emotion classification. Three base ML models that work in a distributed manner were used. According to the findings, the ensemble model proposed differentiates between the seven fundamental emotions with reasonable accuracy. The proposed distributed ensemble model performed better than existing ML models on TESS, SAVEE, and RAVDESS, achieving 86% accuracy on the unified dataset.

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Authors and Affiliations

  1. Department of Computer Applications, Cochin University of Science and Technology, Kochi, Kerala, 682022, India

    Bineetha Vijayan, Gayathri Soman, M. V. Vivek & M. V. Judy

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  1. Bineetha Vijayan
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  2. Gayathri Soman
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  3. M. V. Vivek
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  4. M. V. Judy
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Corresponding author

Correspondence to Bineetha Vijayan .

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Editors and Affiliations

  1. Indian Institute of Technology-Roorkee, Roorkee, India

    Partha Pratim Roy

  2. IBM Research, Gurugram, India

    Arvind Agarwal

  3. Southwest Jiaotong University, Chengdu, China

    Tianrui Li

  4. International Institute of Information Technology - Hyderabad, Hyderabad, India

    P. Krishna Reddy

  5. The University of Aizu, Fukushima, Japan

    R. Uday Kiran

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Vijayan, B., Soman, G., Vivek, M.V., Judy, M.V. (2022). A Distributed Ensemble Machine Learning Technique for Emotion Classification from Vocal Cues. In: Roy, P.P., Agarwal, A., Li, T., Krishna Reddy, P., Uday Kiran, R. (eds) Big Data Analytics. BDA 2022. Lecture Notes in Computer Science, vol 13773. Springer, Cham. https://doi.org/10.1007/978-3-031-24094-2_9

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  • DOI: https://doi.org/10.1007/978-3-031-24094-2_9

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