Speech-based automated cognitive status assessment

Verbal interviews performed by trained clinicians are a common form of assessments to measure cognitive decline. The aim in this paper is to study the usability of automated methods for evaluating verbal cognitive status assessment tests for the elderly. If reliable, such methods for cognitive assessment can be used for frequent, non-intrusive, low-cost screenings and provide objective and longitudinal cognitive status monitoring data that can complement regular clinical visits and would be useful for early detection of conditions associated with language and communication impairments. This study focuses on two types of tests: a story-recall test, used for memory and language functioning assessment, and a picture description test, used to assess the information content in speech. A data collection was designed for this study involving recordings of about 100 people, mostly over 70 years old, performing these tests. The speech samples were manually transcribed and annotated with semantic units in order to obtain manual evaluation scores. We explore the use of automatic speech recognition and language processing methods to derive objective, automatically extracted metrics of cognitive status that are highly correlated with the manual scores. We use recall and precision based metrics based on semantic content units associated with the tests. Our experiments show high correlation between manually obtained scores and the automatic metrics obtained using either manual or automatic speech transcriptions.