Name
Abstract | ||
|---|---|---|
Early prediction of neurodegenerative disorders such as Alzheimer's disease (AD) and related dementias may facilitate earlier access to medical and social supports. Further, detection of individuals with preclinical disease may help to enrich clinical trial populations for studies examining disease-modifying interventions.Changes in speech and language patterns may occur in the early stages of neurodegenerative diseases such as AD and frontotemporal dementia, with worsening as the disease progresses. This has led to recent attempts to create automatic methods that predict cognitive impairment and dementia through language analysis. Previous works have improved the prediction accuracy by introducing some task-specific features in addition to task-agnostic linguistic and acoustic features. However, task-specific features prevent the model from generalizing to other tests and languages.In this paper, we focus on exploring the effectiveness of neural network models that require no task-specific feature for dementia prediction in three different ways. First, we use a multi modal neural model to fuse linguistic features and acoustic features, and investigate the performance change compared to simply concatenating these features. Second, we propose a novel coherence feature generated by a neural coherence model, and investigate the predictiveness of this new feature for dementia prediction. Finally, we apply an end-to-end neural method which is free from feature engineering and achieves state-of-the-art classification result on a widely used dementia dataset.(c) 2020 Elsevier Ltd. All rights reserved. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.csl.2020.101181 | COMPUTER SPEECH AND LANGUAGE |
Keywords | DocType | Volume |
Automatic dementia prediction, Multimodal embedding, Coherence model, Hierarchical attention networks | Journal | 68 |
ISSN | Citations | PageRank |
0885-2308 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weirui Kong | 1 | 0 | 0.34 |
| Hyeju Jang | 2 | 7 | 2.92 |
| Giuseppe Carenini | 3 | 1461 | 111.12 |
| Thalia Shoshana Field | 4 | 0 | 0.34 |