  Acoustic analyses of infant vocalizations are valuable for research on speech
development as well as applications in sound classification. Previous studies
have focused on measures of acoustic features based on theories of speech
processing, such spectral and cepstrum-based analyses. More recently,
end-to-end models of deep learning have been developed to take raw speech
signals (acoustic waveforms) as inputs and convolutional neural network layers
to learn representations of speech sounds based on classification tasks. We
applied a recent end-to-end model of sound classification to analyze a
large-scale database of labeled infant and adult vocalizations recorded in
natural settings outside the lab with no control over recording conditions. The
model learned basic classifications like infant versus adult vocalizations,
infant speech-related versus non-speech vocalizations, and canonical versus
non-canonical babbling. The model was trained on recordings of infants ranging
from 3 to 18 months of age, and classification accuracy changed with age as
speech became more distinct and babbling became more speech-like. Further work
is needed to validate and explore the model and dataset, but our results show
how deep learning can be used to measure and investigate speech acquisition and
development, with potential applications in speech pathology and infant
monitoring.
