Input matters in the modeling of early phonetic learning
- Ruolan Li, Program in Neuroscience and Cognitive Science, University of Maryland, College Park, Maryland, United States
- Thomas Schatz, Department of Linguistics & UMIACS, University of Maryland, College Park, Maryland, United States
- Yevgen Matusevych, School of Informatics, University of Edinburgh , Edinburgh, United Kingdom
- Sharon Goldwater, School of Informatics, University of Edinburgh, Edinburgh, United Kingdom
- Naomi Feldman, Linguistics and UMIACS, University of Maryland, College Park, Maryland, United States
AbstractIn acquiring language, differences in input can greatly affect learning outcomes, but which aspects of language learning are most sensitive to input variations, and which are robust, remains debated. A recent modeling study successfully reproduced a phenomenon empirically observed in early phonetic learning–learning about the sounds of the native language in the first year of life–despite using input that differed in quantity and speaker composition from what a typical infant would hear. In this paper, we carry out a direct test of that model's robustness to input variations. We find that, despite what the original result suggested, the learning outcomes are sensitive to properties of the input and that more plausible input leads to a better fit with empirical observations. This has implications for understanding early phonetic learning in infants and underscores the importance of using realistic input in models of language acquisition.