InterSpeech 2021

Detecting Alzheimer's Disease using Interactional and Acoustic features from spontaneous speech
(3 minutes introduction)

Shamila Nasreen (Queen Mary University of London, UK), Julian Hough (Queen Mary University of London, UK), Matthew Purver (Queen Mary University of London, UK)
Alzheimer’s Disease (AD) is a form of Dementia that manifests in cognitive decline including memory, language, and changes in behavior. Speech data has proven valuable for inferring cognitive status, used in many health assessment tasks, and can be easily elicited in natural settings. Much work focuses on analysis using linguistic features; here, we focus on non-linguistic features and their use in distinguishing AD patients from similar-age Non-AD patients with other health conditions in the Carolinas Conversation Collection (CCC) dataset. We used two types of features: patterns of interaction including pausing behaviour and floor control, and acoustic features including pitch, amplitude, energy, and cepstral coefficients. Fusion of the two kinds of features, combined with feature selection, obtains very promising classification results: classification accuracy of 90% using standard models such as support vector machines and logistic regression. We also obtain promising results using interactional features alone (87% accuracy), which can be easily extracted from natural conversations in daily life and thus have the potential for future implementation as a non-invasive method for AD diagnosis and monitoring.