Automated Extraction of Spatio-Semantic Graphs for Identifying Cognitive Impairment
This work addresses the need for automated clinical tools to assess cognitive impairment from speech, though it is incremental as it builds on existing spatio-semantic graph concepts.
The paper tackled the problem of manually tagging content information units (CIUs) for spatio-semantic graphs used in cognitive impairment assessment by proposing an automated extraction method from picture descriptions, resulting in graphs that effectively differentiate between cognitively impaired and unimpaired speakers with comparable or greater group differences than manual methods.
Existing methods for analyzing linguistic content from picture descriptions for assessment of cognitive-linguistic impairment often overlook the participant's visual narrative path, which typically requires eye tracking to assess. Spatio-semantic graphs are a useful tool for analyzing this narrative path from transcripts alone, however they are limited by the need for manual tagging of content information units (CIUs). In this paper, we propose an automated approach for estimation of spatio-semantic graphs (via automated extraction of CIUs) from the Cookie Theft picture commonly used in cognitive-linguistic analyses. The method enables the automatic characterization of the visual semantic path during picture description. Experiments demonstrate that the automatic spatio-semantic graphs effectively differentiate between cognitively impaired and unimpaired speakers. Statistical analyses reveal that the features derived by the automated method produce comparable results to the manual method, with even greater group differences between clinical groups of interest. These results highlight the potential of the automated approach for extracting spatio-semantic features in developing clinical speech models for cognitive impairment assessment.