Although Alzheimer’s disease (AD) is associated with changes in spoken language, these have seldom been subjected to systematic analysis on a large scale.
The LANGaware Method
English and Greek datasets were analyzed employing feature selection techniques to choose the most prominent multi-level linguistic analysis features differentiating the AD from the NC group in both languages. The platform’s diagnostic performance was evaluated on its ability to classify “unseen” audio recordings employing these salient features.
The Results
Evaluation results indicated that LangAware achieved equally high classification scores for both English and Greek. Most significantly, these scores were achieved by employing a custom set of LangAware-developed cross-linguistic markers.
