Reverse engineering infant language development (general theme)

Infants learn their first language effortlessly, without explicit supervision, while being immersed into a complex and noisy environment. Infants do not seem to follow a hierarchical order (sounds, then words, then sentences) as adults would do, but rather, they start learning all of these linguistic levels in parallel.

The aim of our project is to decipher this puzzling learning process by applying a 'reverse engineering ' approach, i.e., by constructing an artificial language learner that mimicks the learning stages of the infant. We use engineering and applied maths techniques (automatic speech recognition, signal processing, machine learning) on large corpora of child-adult verbal interactions in several languages. We develop psychologically plausible (unsupervized) and biologically plausible (bio-inspired) algorithms which can discover linguistic categories (words, syllables, phonemes, features). The validity of these algorithms are then tested in infants or newborns using behavioral techniques (eye tracking) or noninvasive brain imagery (Near InfraRed Spectroscopy, EEGs). 

Here is the list of internship topics proposed by the CoML team for 2021-2022.

Background in maths or linguistics preferable. Background in programming is mandatory.

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