  In the U.S., approximately 15-17% of children 2-8 years of age are estimated
to have at least one diagnosed mental, behavioral or developmental disorder.
However, such disorders often go undiagnosed, and the ability to evaluate and
treat disorders in the first years of life is limited. To analyze infant
developmental changes, previous studies have shown advanced ML models excel at
classifying infant and/or parent vocalizations collected using cell phone,
video, or audio-only recording device like LENA. In this study, we pilot test
the audio component of a new infant wearable multi-modal device that we have
developed called LittleBeats (LB). LB audio pipeline is advanced in that it
provides reliable labels for both speaker diarization and vocalization
classification tasks, compared with other platforms that only record audio
and/or provide speaker diarization labels. We leverage wav2vec 2.0 to obtain
superior and more nuanced results with the LB family audio stream. We use a
bag-of-audio-words method with wav2vec 2.0 features to create high-level
visualizations to understand family-infant vocalization interactions. We
demonstrate that our high-quality visualizations capture major types of family
vocalization interactions, in categories indicative of mental, behavioral, and
developmental health, for both labeled and unlabeled LB audio.
