Alzheimer's Disease Detection from Spontaneous Speech through Combining Linguistic Complexity and (Dis)Fluency Features with Pretrained Language Models

Abstract

In this paper, we combined linguistic complexity and (dis)fluency features with pretrained language models for the task of Alzheimer's disease detection of the 2021 ADReSSo (Alzheimer's Dementia Recognition through Spontaneous Speech) challenge. An accuracy of 83.1% was achieved on the test set, which amounts to an improvement of 4.23% over the baseline model. Our best-performing model that integrated component models using a stacking ensemble technique performed equally well on cross-validation and test data, indicating that it is robust against overfitting.

arXiv commentaccepted at Interspeech2021

References in corpus (5)

• Natural Language Processing (almost) from Scratch
• Convolutional Neural Networks for Sentence Classification
• ERNIE 2.0: A Continual Pre-training Framework for Language Understanding
• Dependency-based Convolutional Neural Networks for Sentence Embedding
• Multi-Layer Ensembling Techniques for Multilingual Intent Classification

Cited by in corpus (1)

• LLMCARE: early detection of cognitive impairment via transformer models enhanced by LLM-generated synthetic data