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

Visualizations of Complex Sequences of Family-Infant Vocalizations Using Bag-of-Audio-Words Approach Based on Wav2vec 2.0 Features