Cited by 16Voice and Speech DisordersSpeech Recognition and SynthesisArtificial Intelligence in Healthcare

A triplet multimodel transfer learning network for speech disorder screening of Parkinson’s disease

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Zhao, Aite, Wang, Nana, Niu, Xuesen, Chen, Ming, Wu, Huimin·2024·International Journal of Intelligent Systems

Abstract

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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Crossref · 10.1155/2024/8890592

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  • Existing acoustic-based approaches such as NAA and TMTLN fail to simultaneously characterize articulation, cognition, and behavior issues in speech disorders because they focus on low-level speech abnormalities and neglect neurolinguistic semantic inconsistency.

    “The limitation of existing acoustic-based approaches such as NAA and TMTLN for simultaneously characterizing the issues related to articulation, cognition, and behavior associated with speech disorders is because of their focus on low-level speech abnormalities while failing to consider neurolinguistic inconsistency related to semantics.”

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  1. S. Woo, J. Park, J. Y. Lee, and I. S. Kweon, "Cbam: con- volutional block attention module," in European Conference on Computer Vision, Springer, Singapore, 2018.

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Authors
Zhao, A., Wang, N., Niu, X., Chen, M., & Wu, H.
Affiliations
Qingdao University (CN)
Corresponding author
Zhao, Aite
Venue
International Journal of Intelligent Systems
Publisher
Wiley
Published
2024-03-20
Volume
2024
Pages
1-20
ISSN
  • 0884-8173 (ISSN-L)
  • 1098-111X
OpenAlex
W4392977357
Cited by
16
References
53
Citations by year
2026: 4 · 2025: 11 · 2024: 1
FWCI
4.56
Type
journal article
Language
en
Open access
Locations
International Journal of Intelligent Systems (publishedVersion, cc-by)
Funders
  • National Natural Science Foundation of China (62106117, 2022M711741, ZR2021QF084, 2023KJ365)
  • China Postdoctoral Science Foundation (62106117, 2022M711741, ZR2021QF084, 2023KJ365)
  • Natural Science Foundation of Shandong Province (62106117, 2022M711741, ZR2021QF084, 2023KJ365)
  • Shandong Province Higher Education Institutions Youth Innovation and Technology Support Program (62106117, 2022M711741, ZR2021QF084, 2023KJ365)
Topics
  • Health Sciences › Medicine › Physiology › Voice and Speech Disorders
  • Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
Parkinson's disease, early diagnosis, triplet loss, spontaneous dialogue, Mel-frequency cepstral coefficients, speech feature extraction, fine-tuning

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