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在帕金森病检测中对预训练的语音嵌入进行排名:Wav2Vec 2.0是否在语音模式和语言中优于其1.0版本?

Ondrej Klempir¹, Adela Skryjova¹, Ales Tichopad²

¹Department of Biomedical Informatics, Faculty of Biomedical Engineering, Czech Technical University in Prague, Sitna Square 3105, Kladno, Czech Republic.

Computational and structural biotechnology journal

|June 30, 2025

概括

与Wav2Vec 1.0相比,Wav2Vec 2.0在各种语音类型中显示了对帕金森病 (PD) 检测的卓越性能. 结合两种模型的层次,进一步提高了诊断准确度,特别是基于母音的分类.

科学领域:

语音和语言技术的使用.
计算语言学计算语言学
生物医学信号处理

背景情况:

帕金森病 (PD) 的诊断可以通过语音分析来改进.
像Wav2Vec这样的自我监督语音预训模型为PD检测提供了先进的功能.
需要对Wav2Vec 1.0和Wav2Vec 2.0进行直接比较,以检测PD.

研究的目的:

系统地比较Wav2Vec 1.0和Wav2Vec 2.0用于对帕金森病 (PD) 受影响的语言进行分类.
评估这些模型在不同语音模式中与传统方法相比的性能.
为了确定PD检测的最佳Wav2Vec架构和层.

主要方法:

利用三个多语言数据集来对健康对照 (HC) 和PD语音进行分类.
使用 Wav2Vec 1.0 和 Wav2Vec 2.0 嵌入式的各种分类方法.
与传统特征进行基准测试,并使用TOPSIS进行排名方法.
分析了自发言语,非自发言语和孤立元音的表现.

主要成果:

在所有语音模式中,Wav2Vec 2.0的表现普遍优于Wav2Vec 1.0.
Wav2Vec 2.0 的第一个变压器层最适合阅读文本和独白.
Wav2Vec 2.0的功能提取器在基于母音的分类中表现出色.
Wav2Vec 1.0 提供了一个具有竞争力和更高效的替代方案.
结合两种模型的层次,提高了母音分类的诊断准确性.

结论:

Wav2Vec 2.0 显示了PD语音检测的显著优势.
Wav2Vec 1.0仍然是一个可行的和高效的选择.
该研究提供了对优化Wav2Vec架构用于PD诊断的见解.
组合模型方法在特定的分类任务中有望提高准确性.

关键词:

分类分类分类分类.帕金森病的疾病.语音模式语音模式波量2vec 1.0 波量2vec 1.0 波量2vec 1.0 波量2vec 1.0 波量2vec 1.0 波量2vec 1.0 波量2vec 1.0 波量2vec 波2vec 2.0 波2vec 2.0 波2vec 2.0 波2vec 2.0 波2vec 2.0 波2vec 2.0 波2vec 2.0 波2vec 2.0 波2vec 2.0 波2vec 2.0 波2vec 2.0 波2vec 2.0

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