Many adults over 60 years live with some form of hearing impairment that makes speech comprehension difficulty. However, progress in predicting speech-comprehension difficulties in everyday life has been limited, in part, because hearing-science research has mainly focused on speech comprehension of short, disconnected sentences that lack a topical thread and are not relevant to the listener. New approaches to understanding naturalistic speech listening are thus critical to gaining insight into impaired speech processing.

The student will be involved in research that leverages novel natural language processing (NLP) approaches (e.g., sentence embeddings) with graph theoretic approaches (e.g., network centrality) to better understand how individuals listen to naturalistic speech. The student will analyze transcripts of spoken stories and transcripts of individuals recalling these stories after listening to them (using NLP), and will integrate relevant information from these analyses to capture the structure in which individuals comprehend speech (using graph theory). The student will program the analyses and visualize the results using Python/MATLAB. The student will work with the supervisor and a graduate student with biophysics and psychology background. The lab provides ample opportunities to learn how sophisticated data-analysis tools can be used to facilitate research in basic science with clinical applicability.