Fanli Business School, Nanyang Institute of Technology, Nanyang, 473004, China.
School of Computer and Software, Nanyang Institute of Technology, Nanyang, 473004, China.
Sci Rep. 2024 Jul 31;14(1):17696. doi: 10.1038/s41598-024-68864-z.
Speech emotion recognition (SER) technology involves feature extraction and prediction models. However, recognition efficiency tends to decrease because of gender differences and the large number of extracted features. Consequently, this paper introduces a SER system based on gender. First, gender and emotion features are extracted from speech signals to develop gender recognition and emotion classification models. Second, according to gender differences, distinct emotion recognition models are established for male and female speakers. The gender of speakers is determined before executing the corresponding emotion model. Third, the accuracy of these emotion models is enhanced by utilizing an advanced differential evolution algorithm (ADE) to select optimal features. ADE incorporates new difference vectors, mutation operators, and position learning, which effectively balance global and local searches. A new position repairing method is proposed to address gender differences. Finally, experiments on four English datasets demonstrate that ADE is superior to comparison algorithms in recognition accuracy, recall, precision, F1-score, the number of used features and execution time. The findings highlight the significance of gender in refining emotion models, while mel-frequency cepstral coefficients are important factors in gender differences.
语音情感识别（SER）技术涉及特征提取和预测模型。然而，由于性别差异和提取的特征数量众多，识别效率往往会降低。因此，本文介绍了一种基于性别的 SER 系统。首先，从语音信号中提取性别和情感特征，以开发性别识别和情感分类模型。其次，根据性别差异，为男性和女性说话者建立不同的情感识别模型。在执行相应的情感模型之前，确定说话者的性别。第三，利用先进的差分进化算法（ADE）选择最优特征来提高这些情感模型的准确性。ADE 采用了新的差分向量、变异算子和位置学习，有效地平衡了全局搜索和局部搜索。提出了一种新的位置修复方法来解决性别差异问题。最后，在四个英语数据集上的实验表明，在识别精度、召回率、精度、F1 分数、使用特征的数量和执行时间方面，ADE 优于对比算法。研究结果表明，性别在细化情感模型方面具有重要意义，而梅尔频率倒谱系数是性别差异的重要因素。
