Motherese Directed at Prelinguistic Infants at Risk for Neurological Disorders: An Exploratory Study.

To investigate how a high risk for infant neurological impairment affects the quality of infant verbal interactions, and in particular properties of infant-directed speech, spontaneous interactions between 14 mothers and their 4.5-month-old infants at high risk for neurological disorders (7 female) were recorded and acoustically compared with those of 14 dyads with typically developing infants (8 female). Mothers of at-risk infants had proportionally less voicing, and the proportion of voicing decreased with increasing severity of the infants' long-term outcome.

Simulating prenatal language exposure in computational models: An exploration study.

Researchers have hypothesized that infant language learning starts from the third trimester of pregnancy. This is supported by studies with fetuses and newborns showing discrimination/preference for their native language. Jointly with empirical research, initial computational modeling studies have investigated whether learning language patterns from speech input benefits from auditory prenatal language exposure (PLE), showing some advantages for prior adaptation to speech-like patterns.

Establishing the reliability of metrics extracted from long-form recordings using LENA and the ACLEW pipeline.

Long-form audio recordings are increasingly used to study individual variation, group differences, and many other topics in theoretical and applied fields of developmental science, particularly for the description of children's language input (typically speech from adults) and children's language output (ranging from babble to sentences). The proprietary LENA software has been available for over a decade, and with it, users have come to rely on derived metrics like adult word count (AWC) and child vocalization counts (CVC), which have also more recently been derived using an open-source alternative, the ACLEW pipeline. Yet, there is relatively little work assessing the reliability of long-form metrics in terms of the stability of individual differences across time.

Automatic assessment of infant carrying and holding using at-home wearable recordings.

Assessing infant carrying and holding (C/H), or physical infant-caregiver interaction, is important for a wide range of contexts in development research. An automated detection and quantification of infant C/H is particularly needed in long term at-home studies where development of infants' neurobehavior is measured using wearable devices. Here, we first developed a phenomenological categorization for physical infant-caregiver interactions to support five different definitions of C/H behaviors.

Evaluation of self-supervised pre-training for automatic infant movement classification using wearable movement sensors.

The recently-developed infant wearable MAIJU provides a means to automatically evaluate infants' motor performance in an objective and scalable manner in out-of-hospital settings. This information could be used for developmental research and to support clinical decision-making, such as detection of developmental problems and guiding of their therapeutic interventions. MAIJU-based analyses rely fully on the classification of infant's posture and movement; it is hence essential to study ways to increase the accuracy of such classifications, aiming to increase the reliability and robustness of the automated analysis.