Before a child says their first word, they begin to produce and practice sounds they hear. Early vocalizations play a crucial role in speech development and language acquisition. However, most research on infant vocalizations focuses on children in Western, industrialized societies. This study contributes to the growing body of literature on diverse linguistic environments, specifically examining emergent sounds in the Panãra community, an Indigenous group in the Brazilian Amazon with approximately 700 speakers. Ten infants aged 2-21 months wore recording devices that collected a recording of their language environment over a day. Alongside shared ethnographic observations, I manually annotated selected 30-second audio segments for a fine-grained analysis of child vocalizations. I am currently analyzing the frequency and types of child vocalizations (i.e. vocal play, canonical babbling, variegated babbling) in infants' speech, and I plan to explore how these vocalizations may differ across the age range studied. I predict that child vocalizations will become more complex with increasing age, following pre-speech vocal development stages broadly found across cultures. My findings will contribute to a broader understanding of how language learning varies across cultural settings, vocalization stages, and the role of the environment to language development. 

Language input is necessary for language development. Importantly, mothers have been shown to speak to infants more than fathers do. My study asks whether this pattern extends to the amount of music that mothers produce or play to infants. Music impacts people neurologically, emotionally, and even physically, and can possibly be used to enhance the linguistic development of infants alongside speech. I am comparing the amount and type of speech and music heard by infants in mother-father families to infants in mother-mother families to isolate the variable of gender and gauge its association with infants’ auditory input. Daylong Language ENvironment Analysis (LENA) recorders are used to record everything in an infant’s naturalistic environment (at home) therefore capturing how many instances of in-person and/or electronic speech or music occur and whether parents’ speaking and/or singing is directed to the infants. Undergraduate students are currently annotating LENA recordings of twenty-one mother-mother families (ages 3-24 months) and twenty-three mother-father families (ages 6-24 months) for the amount and type of speech and music present in infants’ audio environments. Annotators indicate what is heard in 100 randomly sampled 10-second segments from each daylong recording. Using independent samples t-tests, I am analyzing the differences in the average amount of music, the average amount of speech, and the type of music presented to infants of mother-mother families versus infants of mother-father families. I hypothesize that there is significantly more speech and music heard by infants in mother-mother families compared to infants in mother-father families. I also hypothesize there is significantly more singing heard by infants of mother-mother dyads, but a comparable amount of electronic music. If found, these results will point to gender being associated with auditory input variability, expanding the knowledge on environmental factors that influence infant language development.

In researching language development, it is important to observe a child in their natural environment instead of a lab, because this gives better insight into their daily life and development. Language ENvironment Analysis (LENA) is a recorder often used for such projects which is worn by the child and collects up to 16 hours of sound. Although LENA creates automatic estimates of various statistics, such as number of adult/child words and changes in speaker, other variables, such as how much speech is directed to the child (as opposed to overheard) must be manually annotated by humans, which is time-consuming and expensive. Recently, researchers developed an open-source classifier that uses LENA’s estimates to identify segments of recordings as sleep, child-directed speech (CDS), or other-directed speech (ODS) (Bang et al., 2023). If accurate, this technology could significantly speed up the annotation process, potentially enhancing the scope of language interventions. My research focuses on verifying the reliability of the classifier and its validity for use in future research. I am in the process of reanalyzing a previously published dataset of daylong LENA recordings collected with infants 6-24 months of age. I processed the original LENA data through the new classifier and currently oversee undergraduates who manually annotate a random selection of the segments, which I compare with the classifier. My preliminary findings show that the classifier’s reliability is limited for recordings collected with the youngest infants; however, I hypothesize to find higher reliability at older ages, since LENA’s automatic statistics are more accurate for recordings from older ages. I am also investigating which other aspects of the segments affect the reliability of the classifier (such as presence of additional children, noise, etc.). My results will give insight into if, and in what contexts, the classifier can be used for future research.