The multi timescale phoneme acquisition model of the self-organizing based on the dynamic features

It is unclear as to how infants learn the acoustic expression of each phoneme of their native languages. In recent studies, researchers have inspected phoneme acquisition by using a computational model. However, these studies have used a limited vocabulary as input and do not handle a continuous speech that is almost comparable to a natural environment. Therefore, we use a natural continuous speech and build a self-organization model that simulates the cognitive ability of the humans, and we analyze the quality and quantity of the speech information that is necessary for the acquisition of the native phoneme system. Our model is designed to learn values of the acoustic features of a continuous speech and to estimate the number and boundaries of the phoneme categories without using explicit instructions. In a recent study, our model could acquire the detailed vowels of the input language. In this study, we examined the mechanism necessary for an infant to acquire all the phonemes of a language, including consonants. In natural speech, vowels have a stationary feature; hence, our recent model is suitable for learning them. However, learning consonants through the past model is difficult because most consonants have more dynamic features than vowels. To solve this problem, we designed a method to separate "stable" and "dynamic" speech patterns using a feature-extraction method based on the auditory expressions used by human beings. Using this method, we showed that the acquisition of an unstable phoneme was possible without the use of instructions.