SPEECH SEGMENTATION IN A SIMULATED BILINGUAL ENVIRONMENT: A CHALLENGE FOR STATISTICAL LEARNING?

Studies using artificial language streams indicate that infants and adults can use statistics to correctly segment words. However, most studies have utilized only a single input language. Given the prevalence of bilingualism, how is multiple language input segmented? One particular problem may occur if learners combine input across languages: the statistics of particular units that overlap different languages may subsequently change and disrupt correct segmentation. Our study addresses this issue by employing artificial language streams to simulate the earliest stages of segmentation in adult L2-learners. In four experiments, participants tracked multiple sets of statistics for two artificial languages. Our results demonstrate that adult learners can track two sets of statistics simultaneously, suggesting that they form multiple representations when confronted with bilingual input. This work, along with planned infant experiments, informs a central issue in bilingualism research, namely, determining at what point listeners can form multiple representations when exposed to multiple languages.