对于性别意识的高级差异进化英语语音情感识别认知
Liya Yue1, Pei Hu2, Jiulong Zhu3
1Fanli Business School, Nanyang Institute of Technology, Nanyang, 473004, China.
Scientific reports
|July 31, 2024
概括
这项研究介绍了一种基于性别的语音情感识别 (SER) 系统. 通过为男性和女性扬声器开发不同的模型,并通过先进的差异演化算法 (ADE) 优化功能,该系统显著提高了识别精度.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 语音情感识别 (SER) 系统因性别差异和众多提取的特征而面临挑战,影响识别效率.
- 现有的SER模型经常难以解释性别之间的语音模式变化,导致表现不佳.
- 从语音信号中提取的大量特征也可能导致识别精度降低和计算负载增加.
研究的目的:
- 开发一种新的SER系统,以解决性别特异性的语音变化,以提高情感识别准确度.
- 引入先进的差异演化算法 (ADE) 以在SER中进行最佳特征选择,提高模型性能.
- 研究性别特异性情感识别模型对整体系统有效性的影响.
主要方法:
- 设计了一个SER系统,结合了性别识别和情绪分类模型.
- 为男性和女性演讲者开发了不同的情感识别模型,性别识别先于情感分类.
- 一种先进的差异演化算法 (ADE),具有新的差异向量,突变运算符,位置学习和一种新的修复方法,用于最佳特征选择.
主要成果:
- 在四个英语数据集上的实验表明,拟议的ADE算法优于比较方法.
- 基于ADE的SER系统实现了更高的识别精度,回忆精度和F1分数.
- 该系统还在使用的功能数量和执行时间方面取得了改进,突出显示了其效率.
结论:
- 性别是完善语音情感识别模型的重要因素,性别特定的方法提高了表现.
- 提出的高级差异演化算法 (ADE) 有效地优化了SER的特征选择,平衡了全球和本地搜索.
- 梅尔频率塞普斯特尔系数 (MFCCs) 被确定为在语音情感识别中导致性别差异的重要因素.
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