Parkinson's Disease Detection Method Based on Cross-Language Acoustic Analysis

JI Wei; WANG Chuanyu; WU Di; LI Yun; ZHENG Huifen

doi:10.11999/JEIT230981

Volume 46 Issue 2

Feb. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(2): 546-554

JI Wei, WANG Chuanyu, WU Di, LI Yun, ZHENG Huifen. Parkinson's Disease Detection Method Based on Cross-Language Acoustic Analysis[J]. Journal of Electronics & Information Technology, 2024, 46(2): 546-554. doi: 10.11999/JEIT230981

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JI Wei, WANG Chuanyu, WU Di, LI Yun, ZHENG Huifen. Parkinson's Disease Detection Method Based on Cross-Language Acoustic Analysis[J]. Journal of Electronics & Information Technology, 2024, 46(2): 546-554. doi: 10.11999/JEIT230981

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Parkinson's Disease Detection Method Based on Cross-Language Acoustic Analysis

doi: 10.11999/JEIT230981

JI Wei¹,
WANG Chuanyu¹,
WU Di¹,
LI Yun^{2
,
,},
ZHENG Huifen³

1.
School of Communication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
2.
School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
3.
Affiliated Geriatric Hospital of Nanjing Medical University, Nanjing 210024, China

Funds: The Basic Scientific (Natural Science) Major Program of the Higher Education Institutions of Jiangsu Province, China (21KJA520003)

Received Date: 2023-09-07
Rev Recd Date: 2023-12-04

Available Online: 2023-12-13

Publish Date: 2024-02-29

Abstract

Abstract

The research on speech-based Parkinson’s disease detection has the advantages of non-intrusive, low cost and non-invasive. The current publicly available speech datasets for Parkinson’s disease mostly originate from single-language speech, which has the characteristics such as insufficient data capacity and small differences in the pronunciation characteristics of the subjects' mother tongue. The Parkinson’s disease detection model trained on a single language dataset will experience performance degradation when faced with cross-language speech data. To avoid the impact of language differences and improve the detection performance of the model in cross-language scenarios, the ideas of adversarial transfer learning and feature decoupling is introduced and a Parkinson’s disease Cross-Language Speech Analysis Model (CLSAM) is proposed in this paper. Firstly, the model cascades a multihead self-attention encoder and a multi-layer neural network to form a feature extractor module, which is used to decouple the original Fbank speech features extracted from the pronunciation characteristics of the source domain and target domain into two vectors, namely domain invariant pathological information representation vector and domain information representation vector. Secondly, a dual adversarial training module with inconsistent target tasks is designed, which explicitly separates domain invariant pathological information and domain information. Finally, domain invariant pathological information is extracted from cross-language speech data for Parkinson’s disease detection. This paper verifies the effectiveness of the proposed method using a ten-fold cross-validation method on both the publicly available MaxLittle Parkinson’s disease speech dataset and the self-collected Parkinson’s disease speech dataset. Experimental results show that compared with traditional machine learning methods and existing transfer learning algorithms, the proposed model significantly improves the accuracy, sensitivity and F1 scores in cross-language scenarios.
- Cross-language speech analysis,
- Parkinson’s disease,
- Adversarial transfer learning,
- Feature decoupling

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References(25)

References

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