Bridging Machine Learning and Event‐Based Analyses to Assess Maternal Contingent Responses to Infant Nondistress and Distress Vocalizations From Home Audio Recordings

ABSTRACT

Caregiver‐infant vocal interactions are foundational for early development, yet most evidence is derived from brief laboratory or home‐visit observations and assessing caregiver–infant vocal contingency in everyday settings requires scalable methods. To this end, we integrated machine learning (ML) with traditional event‐based analyses to measure maternal contingent vocal responsiveness assessed from daylong audio recordings in the home. In Study 1 ( N = 61 infants; 1–18 months, M _age = 9.43 months), we evaluated a wav2vec2‐based audio tagging algorithm to detect infant nondistress and distress vocalizations and caregiver vocalizations. Performance was high when tested against human annotations. In Study 2 ( N = 56 infants; 1–10 months; M _age = 6.61 months), we applied the algorithm to daylong home audio recordings. Maternal response rates based on ML‐generated labels showed strong correspondence with those based on human‐annotated labels, although a consistent positive bias was observed (ML > human). Response‐rate distributions tended toward higher values in the 20 most voluble segments relative to all available mother–infant interaction segments. Additionally, on average, the 20 most voluble segments (vs all available segments) produced higher maternal response rates but lower base‐rate‐adjusted estimates of contingency. Response rates increased with lag window, and in most cases, the sharpest increase was observed between 1 and 2 s, with diminishing returns thereafter. Together, these findings support ML‐derived vocalization labels for scalable contingency estimation and underscore analytic decisions regarding sampling and lag window when estimating maternal contingent responsiveness from daylong home recordings.