Parkinsonian Chinese Speech Analysis towards Automatic Classification of Parkinson's Disease

Hao Fang, Chen Gong, Chen Zhang, Yanan Sui, Luming Li
Proceedings of the Machine Learning for Health NeurIPS Workshop, PMLR 136:114-125, 2020.

Abstract

Speech disorders often occur at the early stage of Parkinson’s disease (PD). The speech impairments could be indicators of the disorder for early diagnosis, while motor symptoms are not obvious. In this study, we constructed a new speech corpus of Mandarin Chinese and addressed classification of patients with PD. We implemented classical machine learning methods with ranking algorithms for feature selection, convolutional and recurrent deep networks, and an end to end system. Our classification accuracy significantly surpassed state-of-the-art studies. The result suggests that free talk has stronger classification power than standard speech tasks, which could help the design of future speech tasks for efficient early diagnosis of the disease. Based on existing classification methods and our natural speech study, the automatic detection of PD from daily conversation could be accessible to the majority of the clinical population.

Cite this Paper


BibTeX
@InProceedings{pmlr-v136-fang20a, title = {Parkinsonian {C}hinese Speech Analysis towards Automatic Classification of {P}arkinson's Disease}, author = {Fang, Hao and Gong, Chen and Zhang, Chen and Sui, Yanan and Li, Luming}, booktitle = {Proceedings of the Machine Learning for Health NeurIPS Workshop}, pages = {114--125}, year = {2020}, editor = {Emily Alsentzer and Matthew B. A. McDermott and Fabian Falck and Suproteem K. Sarkar and Subhrajit Roy and Stephanie L. Hyland}, volume = {136}, series = {Proceedings of Machine Learning Research}, month = {11 Dec}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v136/fang20a/fang20a.pdf}, url = {http://proceedings.mlr.press/v136/fang20a.html}, abstract = {Speech disorders often occur at the early stage of Parkinson’s disease (PD). The speech impairments could be indicators of the disorder for early diagnosis, while motor symptoms are not obvious. In this study, we constructed a new speech corpus of Mandarin Chinese and addressed classification of patients with PD. We implemented classical machine learning methods with ranking algorithms for feature selection, convolutional and recurrent deep networks, and an end to end system. Our classification accuracy significantly surpassed state-of-the-art studies. The result suggests that free talk has stronger classification power than standard speech tasks, which could help the design of future speech tasks for efficient early diagnosis of the disease. Based on existing classification methods and our natural speech study, the automatic detection of PD from daily conversation could be accessible to the majority of the clinical population.} }
Endnote
%0 Conference Paper %T Parkinsonian Chinese Speech Analysis towards Automatic Classification of Parkinson's Disease %A Hao Fang %A Chen Gong %A Chen Zhang %A Yanan Sui %A Luming Li %B Proceedings of the Machine Learning for Health NeurIPS Workshop %C Proceedings of Machine Learning Research %D 2020 %E Emily Alsentzer %E Matthew B. A. McDermott %E Fabian Falck %E Suproteem K. Sarkar %E Subhrajit Roy %E Stephanie L. Hyland %F pmlr-v136-fang20a %I PMLR %P 114--125 %U http://proceedings.mlr.press/v136/fang20a.html %V 136 %X Speech disorders often occur at the early stage of Parkinson’s disease (PD). The speech impairments could be indicators of the disorder for early diagnosis, while motor symptoms are not obvious. In this study, we constructed a new speech corpus of Mandarin Chinese and addressed classification of patients with PD. We implemented classical machine learning methods with ranking algorithms for feature selection, convolutional and recurrent deep networks, and an end to end system. Our classification accuracy significantly surpassed state-of-the-art studies. The result suggests that free talk has stronger classification power than standard speech tasks, which could help the design of future speech tasks for efficient early diagnosis of the disease. Based on existing classification methods and our natural speech study, the automatic detection of PD from daily conversation could be accessible to the majority of the clinical population.
APA
Fang, H., Gong, C., Zhang, C., Sui, Y. & Li, L.. (2020). Parkinsonian Chinese Speech Analysis towards Automatic Classification of Parkinson's Disease. Proceedings of the Machine Learning for Health NeurIPS Workshop, in Proceedings of Machine Learning Research 136:114-125 Available from http://proceedings.mlr.press/v136/fang20a.html.

Related Material