  One of the many tasks facing the typically-developing child language learner
is learning to discriminate between the distinctive sounds that make up words
in their native language. Here we investigate whether multimodal
information--specifically adult speech coupled with video frames of speakers'
faces--benefits a computational model of phonetic learning. We introduce a
method for creating high-quality synthetic videos of speakers' faces for an
existing audio corpus. Our learning model, when both trained and tested on
audiovisual inputs, achieves up to a 8.1% relative improvement on a phoneme
discrimination battery compared to a model trained and tested on audio-only
input. It also outperforms the audio model by up to 3.9% when both are tested
on audio-only data, suggesting that visual information facilitates the
acquisition of acoustic distinctions. Visual information is especially
beneficial in noisy audio environments, where an audiovisual model closes 67%
of the loss in discrimination performance of the audio model in noise relative
to a non-noisy environment. These results demonstrate that visual information
benefits an ideal learner and illustrate some of the ways that children might
be able to leverage visual cues when learning to discriminate speech sounds.
