分析和研究基于光谱的情感语音信号增强算法
Huawei Tao1,2,3, Sixian Li1,2, Xuemei Wang1,2
1Key Laboratory of Grain Information Processing and Control, Henan University of Technology, Ministry of Education, Zhengzhou 450001, China.
Entropy (Basel, Switzerland)
|June 26, 2025
概括
语音情感识别中的数据增强如果不仔细选择,可能会损害表现. 反响和重新采样是有效的,在不扭曲情感标签的情况下,提高了高达7.1%的准确性.
科学领域:
- 语音处理 语音处理
- 机器学习是机器学习.
- 情感计算是一种情感计算.
背景情况:
- 数据增强对于改善语音情感识别 (SER) 模型至关重要.
- 增强对情感语音数据的影响仍未得到充分研究,有可能导致标签扭曲和性能下降.
研究的目的:
- 系统地评估常见的数据增强技术对SER的影响.
- 识别增强方法,以增强数据的多样性,而不损害情感完整性.
主要方法:
- 主观的听觉实验来评估情绪表达的变化.
- 从光谱图和热图可视化中提取多维特征.
- 使用交叉损失和统计显著性测试进行客观评估.
主要成果:
- 时间延伸显著扭曲语音特征,对情绪识别准确性产生负面影响.
- "反响" (RIR) 和"重新采样"对情绪标签的影响最小.
- 结合声和重新采样,模型的准确性提高了高达7.1%.
结论:
- 仔细选择数据增强对于有效的SER至关重要.
- 反响和重新采样是增强SER数据集的有希望的技术.
- 这些发现为优化 SER 增强策略提供了基础.
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