Computational models of early language acquisition and the role of different voices

Abstract

Babies learn words from the speakers in their environment. This project / thesis was inspired by the question how babies can discover words in the speech signal. This signal is continuous, unlabelled, and not divided into shorter units that coincide with sound- or word-boundaries. So far, research on infants' input concentrated on the main caregiver, usually the mother. The assumption was that the main caregiver provides most of the information babies need to learn their native language. The role of other speakers as largely been unknown. This project used computational models to simulate the language acquisition process. This allowed for full control over the input and over all processes inside the simulated baby's mind. Computer models learned words from real speech, without intervening processed that described the continuous signal in terms of single sounds or words. It turned out that these models could learn words and that we can even simulate babies in experiments successfully with such a model. Additional studies revealed that hearing many speakers, both men and women, can help the word learning process. Hearing variable input led to very successful word learning overall. Especially in tricky conditions, such as noise or encountering yet another unknown speaker, the models who learned from many speakers usually fared better. In conclusion, variability and different voices in the speech signal can be very valuable when learning words.