A multi-institutional team of cognitive scientists and computational linguists has developed a quantitative modeling framework based on large-scale simulation of infants' language learning process.
The approach uses machine learning to enable the systematic linkage of learning mechanisms to testable predictions about infants' attunement to native language.
The researchers trained a clustering algorithm on realistic speech input to model infants' language learning process; they fed the program spectrogram-like auditory features sampled at regular intervals obtained from naturalistic speech recordings in American English and Japanese.
This resulted in a candidate model for infants' early phonetic knowledge, which the team queried about observed differences in how Japanese- and English-learning infants discriminate speech sounds, as well as vowel- and consonant-like phonetic categories.
The model yielded positive and negative outcomes for these respective queries, suggesting current literature on early phonetic learning requires a dramatic rethink.
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