Pauses for Detection of Alzheimer’s Disease

Yuan, Jiahong; Cai, Xingyu; Bian, Yuchen; Zheng, Yi; Church, Kenneth

doi:10.3389/fcomp.2020.624488

Cited by 18 publications

(22 citation statements)

References 37 publications

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“…They obtained 90% accuracy and demonstrated the importance of dysfluency and pauses in detecting AD. The champion of the ADReSS challenge ( 10 ) combined deep learning with pauses and obtained SOTA accuracy of 89.6%, proving that pauses are important for AD diagnosis. Sadeghian et al ( 41 ) extracted acoustic features, including pauses more than 5 s in duration, and obtained the best accuracy of 95.8%.…”

Section: Discussionmentioning

confidence: 99%

“…The parameters of the distilBert model are presented in Table 4 . The champion of the ADReSS challenge obtained an accuracy of 0.896 by combining the Enhanced Language Representation with Informative Entities (ERNIE) model ( 39 ) and pause information in speech using acoustic align technology ( 10 ). We achieved 88% accuracy on the test set, which is almost equivalent to the SOTA result, and a 13% improvement over the baseline of 75%, established by the organizers of ADReSS ( 9 ).…”

Section: Methodsmentioning

confidence: 99%

“… * ERNIE+Pause ( 10 ) is the model of a champion, distilBert +LR is our method, RF and Ada are the abbreviations of random forest and adaboost classifier, respectively . The best performance in a column of measure .…”

Section: Methodsmentioning

confidence: 99%

“…In addition, a grid search strategy ( 8 ) was used to tune the parameters to obtain the best parameters of the model. The results show that this method worked better on ADReSS datasets ( 9 ) in 2020, with an accuracy of 0.88, which was significantly higher than the baseline and almost equivalent to the best performance on the challenge ( 10 ).…”

Section: Introductionmentioning

confidence: 93%

See 3 more Smart Citations

A Transfer Learning Method for Detecting Alzheimer's Disease Based on Speech and Natural Language Processing

Liu

Luo

Yuan

et al. 2022

Front. Public Health

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Alzheimer's disease (AD) is a neurodegenerative disease that is difficult to be detected using convenient and reliable methods. The language change in patients with AD is an important signal of their cognitive status, which potentially helps in early diagnosis. In this study, we developed a transfer learning model based on speech and natural language processing (NLP) technology for the early diagnosis of AD. The lack of large datasets limits the use of complex neural network models without feature engineering, while transfer learning can effectively solve this problem. The transfer learning model is firstly pre-trained on large text datasets to get the pre-trained language model, and then, based on such a model, an AD classification model is performed on small training sets. Concretely, a distilled bidirectional encoder representation (distilBert) embedding, combined with a logistic regression classifier, is used to distinguish AD from normal controls. The model experiment was evaluated on Alzheimer's dementia recognition through spontaneous speech datasets in 2020, including the balanced 78 healthy controls (HC) and 78 patients with AD. The accuracy of the proposed model is 0.88, which is almost equivalent to the champion score in the challenge and a considerable improvement over the baseline of 75% established by organizers of the challenge. As a result, the transfer learning method in this study improves AD prediction, which does not only reduces the need for feature engineering but also addresses the lack of sufficiently large datasets.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 93%

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A Transfer Learning Method for Detecting Alzheimer's Disease Based on Speech and Natural Language Processing

Liu

Luo

Yuan

et al. 2022

Front. Public Health

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“…There are many more use cases for fine-tuning. We have used fine-tuning in speech for classifying emotions and Alzheimer's disease (Yuan et al 2021b;Yuan et al 2021a). Fine-tuning is likely to become one of the more popular methods for an extremely wide range of use cases in many fields including computational linguistics, speech, and vision.…”

Section: Discussionmentioning

confidence: 99%

Emerging trends: A gentle introduction to fine-tuning

Church¹,

Chen

2021

Nat. Lang. Eng.

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The previous Emerging Trends article (Church et al., 2021. Natural Language Engineering27(5), 631–645.) introduced deep nets to poets. Poets is an imperfect metaphor, intended as a gesture toward inclusion. The future for deep nets will benefit by reaching out to a broad audience of potential users, including people with little or no programming skills, and little interest in training models. That paper focused on inference, the use of pre-trained models, as is, without fine-tuning. The goal of this paper is to make fine-tuning more accessible to a broader audience. Since fine-tuning is more challenging than inference, the examples in this paper will require modest programming skills, as well as access to a GPU. Fine-tuning starts with a general purpose base (foundation) model and uses a small training set of labeled data to produce a model for a specific downstream application. There are many examples of fine-tuning in natural language processing (question answering (SQuAD) and GLUE benchmark), as well as vision and speech.

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