Dementia Detection with Phonetic and Phonological Features

Abstract

In this paper the ADDReSS challenge dataset was used for training and testing a binary classifier designed to diagnose AD. This dataset consists of transcripts of descriptions of the Cookie Theft picture, produced by 54 subjects in the training part and 24 subjects in the test part. Two machine learning experiments were conducted on the task of classifying transcribed speech samples with text samples that were produced by people with AD from those produced by normal subjects. The first experiment showed that, among all the subtypes of phonetic and phonological features covered in this paper, vowels provided the best classification performance. The second experiment that used four feature selection techniques showed with the adopted phonetic and phonological features provided about 0.87 F1 score, that is close to the best performance reported in the address challenge by systems using multiple linguistic levels and machine learning techniques. This result confirms the importance of the covered features as indicators of dementia.