Identifying the dialectal background of American Finnish speakers using a supervised machine-learning model

Abstract

Abstract This study presents results of two experiments using supervised machine-learning models to examine individual Finnish speakers’ dialectal backgrounds. Data come from interviews conducted with heritage speakers of Finnish in northern Wisconsin and are compared to data from the Finnish Dialect Syntax Archive. The models were constructed and then, following successful validation testing, used to identify the dialectal background of five individual American Finnish speakers. Results showed individual variation in dialectal backgrounds and some correlation to speakers’ likely language input. Our approach offers a new methodological tool for examining speakers’ dialectal backgrounds in situations of language contact.