Using automatic speech recognition to identify dementia in early stages

Abstract

Early non-invasive diagnosis of Alzheimer’s disease (AD) and other forms of dementia is a challenging task. Early detection of the symptoms of the disorder could help families and medical professionals prepare for the difficulties ahead, as well as possibly provide a recruitment tool for clinical trials. One possible approach to a non-invasive diagnosis is based on analysis of speech patterns. Subjects are asked to describe a picture and their description (typically 1 to 3 minute speech sample) is recorded. For this study, a database of 70 people were recorded, 24 with a clinical diagnosis of probable or possible Alzheimer’s disease. When these data were combined with 140 other recorded samples, a classifier built with manually transcribed versions of the speech was found to be quite accurate for determining whether or not a speech sample was obtained from an Alzheimer’s patient. A classifier built using automatically determined prosodic features (pitch and energy contours) was also reasonably accurate, with several subsets of pitch and energy features especially effective for classification, as assessed by cross validation. The manually transcribed text has now been replaced by automatically transcribed text using automatic speech recognition (ASR) technology. The main objective of this paper is to report on the relative effectiveness of several ASR approaches, including public domain ones, for this task.