使用预训练模型和集体分类器对语音障碍进行二进制和多类分类的混合方法
Mehtab Ur Rahman1,2, Cem Direkoglu3
1Department of Language and Communication, Radboud University, Houtlaan, Nijmegen, Gelderland, 6525, Netherlands. mehtab.rahman@ru.nl.
BMC medical informatics and decision making
|May 1, 2025
概括
这项研究引入了一种新的混合人工智能 (AI) 方法来分类语音障碍. 人工智能模型在多类语音障碍分类中实现了最先进的准确性,超过了现有的方法.
科学领域:
- 人工智能的人工智能
- 语音处理 语音处理
- 生物声学是一种生物声学.
背景情况:
- 准确分类语音障碍对于诊断和治疗至关重要.
- 当前的人工智能方法在实现高精度方面面临挑战,特别是在多类分类方面.
- 现有的特征提取技术可能无法捕获所有相关的声音特征.
研究的目的:
- 提出和评估一种新的两阶段混合人工智能框架,用于增强语音障碍分类.
- 在二进制和多类语音障碍分类中实现最先进的准确性.
- 通过使用标准语音数据库,将拟议的方法与既有技术进行比较.
主要方法:
- 一种两阶段的混合方法,将深度学习特征与传统分类器结合起来.
- 阶段1:使用预训练的VGGish模型从语音谱图中提取特征嵌入.
- 第二阶段:使用支持矢量机 (SVM),物流回归 (LR),多层感知器 (MLP) 和集体分类器 (EC) 进行分类.
主要成果:
- 混合VGGish-SVM模型在男性 (77.81%) 和组合 (70.53%) 扬声器的多类分类中取得了最高的准确性.
- 对于二进制分类,VGGish-SVM和VGGish-EC分别显示了男性和女性演讲者的最佳表现.
- 建议的混合方法始终优于基于MFCC,MFCC-glottal,wav2vec和HuBERT的方法.
结论:
- 新的混合人工智能框架显示了提高语音障碍分类准确性的巨大潜力.
- 与分类器相结合的VGGish功能嵌入为复杂的语音分析提供了强大的方法.
- 这项工作为开发先进的自动化工具提供了基础,以帮助临床评估语音障碍.
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