在语音情感识别的统计子集分析中PCA和ICA的融合
Rafael Kingeski1, Elisa Henning2, Aleksander S Paterno1
1Center for Science and Technology, Department of Electrical Engineering, Santa Catarina State University (UDESC), Joinville 89219-710, SC, Brazil.
Sensors (Basel, Switzerland)
|September 14, 2024
概括
这项研究探讨了语音情感识别的特征减少. 分离声学特征和应用PCA和ICA等方法提高了模型效率,而不会显著损害准确性.
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) 对人机交互和智能系统至关重要.
- 有效的SER模型需要优化的声学特征集,因为并非所有特征都有同等的贡献.
- 减少特征维度对于高效准确的SER是必不可少的.
研究的目的:
- 在应用减少技术之前,研究将声学特征划分为子集对SER准确度的影响.
- 为了评估过器减少 (克鲁斯卡尔-瓦利斯) 的有效性,然后进行主要组件分析 (PCA) 和独立组件分析 (ICA).
- 为了确定参数特征减少的不分青红白的应用是否会影响SER性能.
主要方法:
- 从三个数据库的语音数据中提取了声学特征:柏林的EmoDB,SAVEE和RAVDESS.
- 根据分布,特征被分为两个子集.
- 克鲁斯卡尔-瓦利斯试验用于过器的缩小,其次是PCA和ICA用于缩小维度.
主要成果:
- 在所有数据库中实现了显著的特征减少.
- 柏林EmoDB:6373个特征减少到170,达到84.3%的准确性.
- SAVEE:以75.4%的准确率减少到130个特征;RAVDESS:以59.9%的准确率减少到150个特征.
结论:
- 功能子集与PCA和ICA等标准减少技术相结合,可以有效地减少SER的维度.
- 这种方法保持了合理的准确性,证明了它在实际应用中的可行性.
- 该研究强调了战略特征选择在开发高效的SER模型中的重要性.
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