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<p>The precise identification and understanding of human emotions by computers is crucial for generating natural interactions between humans and machines. This research presents a novel approach for identifying emotions in speech through the integration of deep learning and metaheuristic techniques. The approach utilizes Deep Maxout Networks (DMN) as the primary framework and enhances it using the modified version of the Water Cycle Algorithm (MWCA). The MWCA enhances the architectural parameters of the DMN and optimizes its capability to recognize emotions from speech signals. The suggested model employs Mel-Frequency Cepstral Coefficients (MFCC) to extract features from speech input, which can enable effective differentiation between numerous emotional states. The efficiency of the model has been assessed using two datasets, CASIA and Emo-DB, achieving an average accuracy of 93.1% and an F1-score of 92.4% on Emo-DB, outperforming baseline models with statistically significant improvements (<i>p</i> &lt; 0.01). This research helps the domain of emotional interaction design by providing a robust tool for computers to understand and react to the emotions of users, and finally improves the general experience of users.</p>

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Improving emotional connection of human and machine using Deep Maxout Networks optimized through Modified Water Cycle optimizer

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月25日

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  • Jun Zhao,
  • Yuanyuan Huang,
  • Mehdi Moattari

摘要

The precise identification and understanding of human emotions by computers is crucial for generating natural interactions between humans and machines. This research presents a novel approach for identifying emotions in speech through the integration of deep learning and metaheuristic techniques. The approach utilizes Deep Maxout Networks (DMN) as the primary framework and enhances it using the modified version of the Water Cycle Algorithm (MWCA). The MWCA enhances the architectural parameters of the DMN and optimizes its capability to recognize emotions from speech signals. The suggested model employs Mel-Frequency Cepstral Coefficients (MFCC) to extract features from speech input, which can enable effective differentiation between numerous emotional states. The efficiency of the model has been assessed using two datasets, CASIA and Emo-DB, achieving an average accuracy of 93.1% and an F1-score of 92.4% on Emo-DB, outperforming baseline models with statistically significant improvements (p < 0.01). This research helps the domain of emotional interaction design by providing a robust tool for computers to understand and react to the emotions of users, and finally improves the general experience of users.