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性别驱动的英语语音情感识别与遗传算法

Liya Yue¹, Pei Hu², Jiulong Zhu¹

¹Fanli Business School, Nanyang Institute of Technology, Nanyang 473004, China.

Biomimetics (Basel, Switzerland)

|June 26, 2024

概括

这项研究引入了一种新的性别情感识别模型,使用遗传算法 (GA) 来增强语音分析. 改进的GA在基于性别的情感识别的准确性和效率方面取得了卓越的表现.

科学领域:

计算机科学计算机科学
人工智能的人工智能
语音处理语音处理

背景情况:

准确的语音情感识别 (SER) 对于个性化的人机交互至关重要.
性别特定的SER增强了在技术,医疗保健和心理学方面的同情心.
现有的模型往往在微妙的情感区分和特征选择方面扎.

研究的目的:

为改进语音分析开发一种新的性别情感识别模型.
使用优化遗传算法 (GA) 增强特征选择和识别精度.
评估模型的性能与最先进的算法对比.

主要方法:

从语音信号中提取性别和情感特征.
基因算法 (GA) 的应用用于高维特征处理和选择.
用基于费舍尔分数特征排名的新型交叉和突变方法改进GA.
与使用支持矢量机器 (SVM) 在四个英语数据集上的现有算法进行比较.

主要成果:

拟议的基于GA的模型在准确性,精度,回忆,F1得分,特征选择和运行时间方面表现出卓越的性能.
梅尔频率塞普斯特拉系数 (MFCC) 和日志MFCC被确定为性别差异化的关键特征.
由于微妙的声音变化,在区分中性,悲伤和恐惧情绪之间仍然存在挑战.

结论:

这种基于GA的新方法显著改善了基于性别的语音情感识别.
基于特征重要性的GA优化提高了模型的效率和准确性.
需要进一步的研究来解决微妙的情绪分类挑战.

关键词:

功能选择功能选择遗传算法是一种遗传算法.这是一个高维的高维空间.语音情感识别语音情感识别

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