An Evaluation on Information Composition in Dementia Detection Based on Speech

In recent years, scientists are paying much attention to the research on automatic dementia detection that could be applied to the speech samples of dementia patients. In a related context, recent research has seen the fast development of Deep Learning (DL) and Natural Language Processing (NLP). The techniques developed for text classification or sentiment analysis have been applied to the field of early dementia detection by many researchers. However, text classification and sentiment analysis are different tasks from dementia detection, which makes us believe that for dementia detection, some adjustments would help improve the performance of the machine learning models. In this work, we implemented experiments with various language models including traditional $n$ -gram language models, Average stochastic gradient descent Weight-Dropped Long Short-Term Memory (AWD-LSTM) models, and attention-based models to evaluate the speech data of dementia patients. Unlike traditional works where the text is stripped from stop words, we propose the idea of exploiting the stop words themselves, since they offer non-context information which helps to identify dementia. As a result, 3 different language models are prepared in this work: a model processing only context words, a model processing stop words and Part-of-Speech (PoS) tag sequences, and a model processing both of them. By performing the aforementioned experiments, we show that both grammar and vocabulary contribute equally to classification: The 3 models achieve an accuracy equal to 70.00%, 76.16%, and 81.54%, respectively.