Abstract

This study proposes a framework by which grammatically and syntactically sound sentences are classified through the perceptual measurement in noise of multilinguals and monolinguals, using an objective measure called SPERI and an interpretivist measure called SPIn, with results evaluated using Shortlist models and the BLINCS model. Hereby filling a knowledge gap on the perception of sentences that combine in varying levels of contextual meaning, linguistic load and cognitive load, this study used sentence clustering methods to find limitations of the proposed framework in determining an absolute and accurate prediction of performance between sentences in the proposed different categories, with factors such as sentence predictability and word frequency taking precedence. There were unintended findings including a relationship between the number of languages spoken and performance, proficiency in other languages decreasing performance despite being an English Native, and how mistakes by multilinguals were more semantically and phonetically influenced than monolinguals.