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使用转换和基于生物模拟智能的优化与机器学习用于语音情感识别的方法框架.

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  • 1Department of Artificial Intelligence Convergence, Chuncheon 24252, Republic of Korea.

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概括
此摘要是机器生成的。

这项研究通过应用新的转换和优化技术来增强语音情感识别 (SER). 奇普莱特转换与驼群算法和双极端学习机器相结合,在将情绪从语音中分类方面取得了高准确性.

关键词:
在ELM中,可以选择ELM.服务服务服务服务服务服务.这是分类分类的分类.功能选择 功能选择变化 转化 转化 转化

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相关实验视频

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科学领域:

  • 人工智能的人工智能
  • 信号处理 信号处理
  • 机器学习 机器学习

背景情况:

  • 语音情感识别 (SER) 对人机交互,医疗应用和娱乐至关重要.
  • 目前的SER方法需要先进的特征提取和选择,以便准确地判断情绪状态.
  • 整合各种信号处理转换与智能优化是推动SER的关键.

研究的目的:

  • 探索六种高级信号转换用于语音情感识别的有效性.
  • 研究特征选择技术的性能,包括重叠信息特征选择 (OIFS) 和仿生算法 (哈里斯·霍克斯优化和驼群算法).
  • 评估各种机器学习模型的分类准确性,特别是极端学习机器 (ELM) 和双极端学习机器 (TELM).

主要方法:

  • 应用了六种转换:同步挤压,分数斯托克威尔 (FST),K-sine转换依赖的集成系统 (KSTDIS),灵活的分析波段 (FAWT),chirplet和超级波段转换为语音信号.
  • 使用重叠信息特征选择 (OIFS),哈里斯·霍克斯优化 (HHO) 和驼群算法 (CSA) 进行特征选择.
  • 在四个数据集 (EMOVO,RAVDESS,SAVEE,柏林Emo-DB) 中使用十个机器学习分类器分类提取的特征,重点是ELM和TELM.

主要成果:

  • 使用CSA和TELM的Chirplet转换在EMOVO数据集上实现了80.63%的准确性.
  • 使用HHO和TELM的FAWT转换在RAVDESS数据集上达到85.76%的准确性.
  • 使用OIFS和TELM的Chirplet变换在SAVEE数据集上获得了83.94%的准确性.
  • 使用CSA和TELM进行的KSTDIS转换在柏林Emo-DB数据集上显示了89.77%的准确性.

结论:

  • 先进的转换,智能特征选择和ELM/TELM分类器的结合显著提高了语音情感识别的准确性.
  • 像CSA和HHO这样的生物仿真优化算法在增强SER任务的特征选择方面显示出强大的潜力.
  • 该研究验证了在多个基准数据集中提出的方法的有效性,为SER系统提供了强大的方法.