用于帕金森病语言障碍筛查的三重多模型迁移学习网络

IF 5 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE International Journal of Intelligent Systems Pub Date : 2024-03-20 DOI:10.1155/2024/8890592
Aite Zhao, Nana Wang, Xuesen Niu, Ming Chen, Huimin Wu
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引用次数: 0

摘要

嗓音和语言质量的下降是帕金森病(PD)的早期征兆。虽然目前已有一些基于计算机的方法利用患者的语音来进行帕金森病的早期诊断,但这些方法只关注固定的发音测试,如通过分析受试者的单音节发音来判断其是否有患帕金森病的潜在可能。此外,仅使用传统语音分析方法提取单视角语音特征并不能提供全面的特征表征。本文专门研究了针对帕金森氏症患者的各种发音测试,包括五个单音节元音的发音和一段自发对话。本文设计并提出了一种三元组多模型迁移学习网络,用于在这两组测试中识别患有帕金森病的受试者。首先,多源数据提取语音的熔频倒频谱系数(MFCC)特征进行预处理。然后,作为迁移学习框架的上游任务,预训练的三元组模型从三个维度表示特征。最后,作为下游任务,将预训练模型重构为一个整合了三元组模型、时序模型和辅助层的新模型,并通过微调更新权重来识别异常语音。实验结果表明,在两组测试中,PD 的最高检测率分别为 99% 和 90%,优于大量国际流行的模式识别算法,可作为该领域其他学术研究人员的基准线。
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A Triplet Multimodel Transfer Learning Network for Speech Disorder Screening of Parkinson’s Disease

Deterioration in the quality of a person’s voice and speech is an early sign of Parkinson’s disease (PD). Although a number of computer-based methods have been invested to use patients’ speech for early diagnosis of Parkinson’s disease, they only focus on a fixed pronunciation test, such as the subjects’ monosyllabic pronunciation is analyzed to determine whether they have potential possibility of PD. Moreover, only using traditional speech analysis methods to extract single-view speech features cannot provide a comprehensive feature representation. This paper is dedicated to the study of various pronunciation tests for patients with PD, including the pronunciation of five monosyllabic vowels and a spontaneous dialogue. A triplet multimodel transfer learning network is designed and proposed for identifying subjects with PD in these two groups of tests. First, multisource data extract mel frequency cepstrum coefficient (MFCC) features of speech for preprocessing. Subsequently, a pretrained triplet model represents features from three dimensions as the upstream task of the transfer learning framework. Finally, the pretrained model is reconstructed as a novel model that integrates the triplet model, temporal model, and auxiliary layer as the downstream task, and weights are updated through fine-tuning to identify abnormal speech. Experimental results show that the highest PD detection rates in the two groups of tests are 99% and 90% , respectively, which outperform a large number of internationally popular pattern recognition algorithms and serve as a baseline for other academic researchers in this field.

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来源期刊
International Journal of Intelligent Systems
International Journal of Intelligent Systems 工程技术-计算机:人工智能
CiteScore
11.30
自引率
14.30%
发文量
304
审稿时长
9 months
期刊介绍: The International Journal of Intelligent Systems serves as a forum for individuals interested in tapping into the vast theories based on intelligent systems construction. With its peer-reviewed format, the journal explores several fascinating editorials written by today''s experts in the field. Because new developments are being introduced each day, there''s much to be learned — examination, analysis creation, information retrieval, man–computer interactions, and more. The International Journal of Intelligent Systems uses charts and illustrations to demonstrate these ground-breaking issues, and encourages readers to share their thoughts and experiences.
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