تقرير
To BERT or Not To BERT: Comparing Speech and Language-based Approaches for Alzheimer's Disease Detection
| العنوان: | To BERT or Not To BERT: Comparing Speech and Language-based Approaches for Alzheimer's Disease Detection |
|---|---|
| المؤلفون: | Balagopalan, Aparna, Eyre, Benjamin, Rudzicz, Frank, Novikova, Jekaterina |
| سنة النشر: | 2020 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computation and Language, Computer Science - Machine Learning |
| الوصف: | Research related to automatically detecting Alzheimer's disease (AD) is important, given the high prevalence of AD and the high cost of traditional methods. Since AD significantly affects the content and acoustics of spontaneous speech, natural language processing and machine learning provide promising techniques for reliably detecting AD. We compare and contrast the performance of two such approaches for AD detection on the recent ADReSS challenge dataset: 1) using domain knowledge-based hand-crafted features that capture linguistic and acoustic phenomena, and 2) fine-tuning Bidirectional Encoder Representations from Transformer (BERT)-based sequence classification models. We also compare multiple feature-based regression models for a neuropsychological score task in the challenge. We observe that fine-tuned BERT models, given the relative importance of linguistics in cognitive impairment detection, outperform feature-based approaches on the AD detection task. Comment: accepted to INTERSPEECH 2020 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2008.01551 |
| رقم الأكسشن: | edsarx.2008.01551 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |