将时间频率异质特征与病态语音检测的交叉注意力机制融合在一起
Zhang Jiaqing1, Wu Yaqin1, Zhang Tao2
1Software College, Shanxi Agricultural University, Taigu 030800, China.
Journal of voice : official journal of the Voice Foundation
|October 2, 2025
概括
这项研究引入了一种用于病态语音检测的新算法,集成各种声学特征以提高准确性和概括性. 新方法增强了多类语音障碍诊断,解决了当前系统中的数据限制.
科学领域:
- 生物医学工程 生物医学工程
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 当前的病理语音诊断系统面临着数据稀缺,特征统一性和有限的模型概括性的挑战.
- 准确检测病态声音对于及时诊断和治疗至关重要.
研究的目的:
- 通过整合时间频率异质声学特征,开发一种用于多类病理语音检测的新算法.
- 增强病理语音诊断模型的稳定性和概括能力.
主要方法:
- 利用Wav2vec2-XLSR从时间域语音信号中进行深度上下文特征提取.
- 集成的Mel频率 Cepstral 系数 (MFCC) 对于异质的声乐特征空间.
- 应用了基于变压器的交叉注意力机制来实现特征对齐和交互.
- 建立了一个双细分度框架,分析了母音和句子等级.
主要成果:
- 在SVD数据集 (句子级) 上,获得了95.1%的准确性,100%的回忆力,0.92的F1得分和0.97的AUC.
- 在MEEI和SVD数据集 (母音级) 上分别获得了100%和99.6%的准确性.
- 通过多体评估,在不同的数据集和数据分布中展示了强大的概括性.
结论:
- 拟议的算法有效地整合了异质的声学特征,以改善病理语音检测.
- 这种新的方法提高了模型的概括性和稳定性,解决了当前系统的关键局限性.
- 这种方法显示了提高语音障碍诊断的准确性和可靠性的巨大潜力.
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