Measuring Spoken Language Variability in Elderly Individuals

The focus of this project is to develop techniques to objectively (automatically) measure spoken language variability and change in aging. Many of the most effective methods for cognitive assessment are mediated by observed behavior, particularly spoken language production. These include clinical instruments, e.g., the Mini Mental Status Examination (MMSE), but also less formal assessments involving interviews or dialogs with physicians or even friends and family. Behavioral changes noted through these spoken language interactions could indicate pathological changes associated with a disorder; or the changes may be transient, due to missing medication or depression at the time of assessment. Alternatively, the observed behavior may be simply due to normal change in spoken language due to aging, or even within the range of natural behavioral variation. Understanding normal versus pathological language change with age requires the collection and annotation of repeated samples from both healthy and impaired individuals. This project has three specific aims: 1) to collect and transcribe longitudinal spoken language sample data elicited in multiple ways from diverse elderly adults; 2) to develop algorithms for automatically extracting features from these spoken language samples; and 3) to characterize the variability of feature values across samples of the same individual; and the utility of feature values and even feature variances for discriminating between subject groups. A particular challenge being addressed by this research is to achieve high-quality, efficient automatic annotation of discourse structure for the spoken language samples. The resulting methods are expected to directly contribute to important behavioral assessment applications.

Funding source

NSF BCS