  During the first years of life, infant vocalizations change considerably, as
infants develop the vocalization skills that enable them to produce speech
sounds. Characterizations based on specific acoustic features, protophone
categories, or phonetic transcription are able to provide a representation of
the sounds infants make at different ages and in different contexts but do not
fully describe how sounds are perceived by listeners, can be inefficient to
obtain at large scales, and are difficult to visualize in two dimensions
without additional statistical processing. Machine-learning-based approaches
provide the opportunity to complement these characterizations with purely
data-driven representations of infant sounds. Here, we use spectral features
extraction and unsupervised machine learning, specifically Uniform Manifold
Approximation (UMAP), to obtain a novel 2-dimensional spatial representation of
infant and caregiver vocalizations extracted from day-long home recordings.
UMAP yields a continuous and well-distributed space conducive to certain
analyses of infant vocal development. For instance, we found that the
dispersion of infant vocalization acoustics within the 2-D space over a day
increased from 3 to 9 months, and then decreased from 9 to 18 months. The
method also permits analysis of similarity between infant and adult
vocalizations, which also shows changes with infant age.
