Towards AI-powered Language Assessment Tools

Abstract

Objectives: The main objective of this paper is to propose an approach for developing an Artificial Intelligence (AI)-powered Language Assessment (LA) tool. Such tools can be used to assess language impairments associated with dementia in older adults. The Machine Learning (ML) classifiers are the main parts of our proposed approach, therefore to develop an accurate tool with high sensitivity and specificity, we consider different binary classifiers and evaluate their performances. We also assess the reliability and validity of our approach by comparing the impact of different types of language tasks, features, and recording media on the performance of ML classifiers. Approach: Our approach includes the following steps: 1) Collecting language datasets or getting access to available language datasets; 2) Extracting linguistic and acoustic features from subjects' speeches which have been collected from subjects with dementia (N=9) and subjects without dementia (N=13); 3) Selecting most informative features and using them to train ML classifiers; and 4) Evaluating the performance of classifiers on distinguishing subjects with dementia from subjects without dementia and select the most accurate classier to be the basis of the AI tool. Results: Our results indicate that 1) we can nd more predictive linguistic markers to distinguish language impairment associated with dementia from participants' speech produced during the Picture Description (PD) language task than the Story Recall (SR) task; and 2) phone-based recording interfaces provide more high-quality language datasets than the web-based recording systems Conclusion: Our results verify that the tree-based classifiers, which have been trained using the linguistic and acoustic features extracted from interviews' transcript and audio, can be used to develop an AI-powered language assessment tool for detecting language impairment associated with dementia.

Control Matters in Elder Care Technology: Evidence and Direction for Designing It In

“…We know you may not have a primary support person now but please think about it in terms of your family member or friend who would care for and look out for you. markers [18,112], which involves audio capture in the home. Location tracking is becoming ubiquitous [25,48], and the problem of social isolation during COVID is further spurring the evolution of telepresence [64,87].…”

“…The nature of data continuously collected about older adults is also rapidly changing, especially as big tech has recently established itself in the space of monitoring and risk assessment of older adults [ 81 , 151 ]. There is particular excitement about the potential capability of detecting cognitive change using predictive linguistic markers [ 18 , 112 ], which involves audio capture in the home. Location tracking is becoming ubiquitous [ 25 , 48 ], and the problem of social isolation during COVID is further spurring the evolution of telepresence [ 64 , 87 ].…”