使用青少年能量和光谱特征融合与特征优化进行压力语音情感识别
Surekha Reddy Bandela1, S Siva Priyanka2, K Sunil Kumar3
1Department of ECE, Institute of Aeronautical Engineering, Hyderabad, India.
Computational intelligence and neuroscience
|October 23, 2023
概括
一种新的特征提取技术,Teager能量自相对应信封 (TEO-Auto-Env),改善了压力语音情感识别. 这种方法与光谱特征相结合,在各种应用中增强了人机界面.
科学领域:
- 语音情感识别 (SER) 是一种语言识别技术.
- 人与计算机的交互
- 信号处理 信号处理
背景情况:
- 语音情感识别 (SER) 对于推进人机接口和评估生理状态至关重要.
- 现有的SER系统在精确检测压力情绪方面面临挑战,原因是语音信号能量减少.
研究的目的:
- 开发一种新的特征提取技术,即Teager能量自相对应信封 (TEO-Auto-Env),用于增强应激情绪检测.
- 将TEO-Auto-Env与已建立的光谱特征集成,以提供强大的压力语音情感识别 (SSER) 系统.
主要方法:
- 提出了一种新的特征提取方法:蒂格尔能量自相关信封 (TEO-Auto-Env).
- 结合的TEO-Auto-Env与Mel频率的角质系数 (MFCC),线性预测角质系数 (LPCC) 和RASTA-PLP光谱特征.
- 在多个数据库 (EMO-DB, EMOVO, IITKGP, EMA) 中使用了k-最近的邻居 (k-NN) 分类器来对性别依赖 (GD) 和语音独立 (SI) 情感进行分类.
主要成果:
- 拟议的SSER系统实现了高分类准确度,TEO-Auto-Env,MFCC和LPCC特征组合的准确度高达95.1%.
- 在不同语言的语音独立 (SI) 和性别依赖 (GD) 案例中,在准确度上观察到显著的改善.
- TEO-Auto-Env有效地增强了对压力语音信号的检测.
结论:
- 将TEO-Auto-Env与光谱特征集成,为强调语音情感识别提供了一种优越的方法.
- 开发的SSER系统表现出增强的性能,为更复杂的人机交互铺平了道路.
- 这种技术对医学,法医等领域的应用具有前景.
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