Simulating prenatal language exposure in computational models: An exploration study.

Abstract

Researchers have hypothesized that infant language learning starts from the third trimester of pregnancy. This is supported by studies with fetuses and newborns showing discrimination/preference for their native language. Jointly with empirical research, initial computational modeling studies have investigated whether learning language patterns from speech input benefits from auditory prenatal language exposure (PLE), showing some advantages for prior adaptation to speech-like patterns. However, these modeling studies have not modeled prenatal speech input in an ecologically representative manner regarding quality or quantity. This study describes an ecologically representative framework for modeling PLE for full-term and preterm infants. The approach is based on empirical estimates of the amount of prenatal speech input together with a model of speech signal attenuation from the external air to the fetus' auditory system. Using this framework, we conduct language learning simulations with computational models that learn from acoustic speech input in an unsupervised manner. We compare the effects of PLE to standard learning from only postnatal input on various early language phenomena. The results show how incorporating PLE can affect models' learning outcomes, including differences between full-term and preterm conditions. Moreover, PLE duration might influence model behavior, depending on the linguistic capability being tested. While the inclusion of PLE did not improve the compatibility of the tested models with empirical infant data, our study highlights the relevance of PLE as a factor in modeling studies. Moreover, it provides a basic framework for modeling the prenatal period in future computational studies.