Everyday language input and production in 1,001 children from six continents

Abstract

Language is a universal human ability, acquired readily by young children, who otherwise struggle with many basics of survival. And yet, language ability is variable across individuals. Naturalistic and experimental observations suggest that children's linguistic skills vary with factors like socioeconomic status and children's gender. But which factors really influence children's day-to-day language use? Here, we leverage speech technology in a big-data approach to report on a unique cross-cultural and diverse data set: >2,500 d-long, child-centered audio-recordings of 1,001 2- to 48-mo-olds from 12 countries spanning six continents across urban, farmer-forager, and subsistence-farming contexts. As expected, age and language-relevant clinical risks and diagnoses predicted how much speech (and speech-like vocalization) children produced. Critically, so too did adult talk in children's environments: Children who heard more talk from adults produced more speech. In contrast to previous conclusions based on more limited sampling methods and a different set of language proxies, socioeconomic status (operationalized as maternal education) was not significantly associated with children's productions over the first 4 y of life, and neither were gender or multilingualism. These findings from large-scale naturalistic data advance our understanding of which factors are robust predictors of variability in the speech behaviors of young learners in a wide range of everyday contexts.

Keywords: human diversity; infancy; language; socioeconomic status; speech.

Fig. 1.

Geographical location, primary language, number of children (N${}_{\mathrm{CHILD}}$), number of recordings (N${}_{\mathrm{REC}}$), and data citation for each corpus.

Fig. 2.

Effects of adult talk, child age, and normative development on children’s speech production. Points show each daylong recording; lines show linear regression with 95% CIs. Child speech is quantified as child linguistic vocalization rate; adult talk as adult vocalization count rate (AVCr). (A) Child speech by age, split by low/mid/high tertiles of adult talk. Lines depict significant adult talk $\times$ age interaction. Color-shape combinations show each unique corpus, numbered to preserve anonymity. (B) Child speech by age and normative status. Lines depict significant age $\times$ normative status interaction. (C) Proportion of vocal behavior classified as speech, cry, or vegetative, by age. The line type/color indicates monolingual and normative statuses. N.B. Monolingual normative CI (blue) falls fully within that for multilingual children (pink) for all three types of vocal behavior, highlighting these groups’ equivalent patterns.

Fig. 3.

Factors that do not predict child speech or adult talk. Points = individual recordings, jittered horizontally. Lines = linear fit with 95% confidence intervals. Error bars = 99% bootstrapped CIs of sample means. Child speech is quantified as child linguistic vocalization rate; adult talk as adult vocalization count rate (AVCr). A & B: null effects of child gender (A) and socioeconomic status (SES) (B) on child speech. (C) Null three-way effect of normative development $\times$ adult talk $\times$ age (N.B.: normative $\times$ age and adult talk $\times$ age are significant; see Fig. 2). (D) null three-way effect of age $\times$ adult talk $\times$ monolingual status. (E and F) null effects of child gender (E) and SES (F) on adult talk. (G and H) null effect of normative development (G) and monolingual status (H) on adult talk.

Fig. 4.

Child speech as a function of SES within individual corpora. SES = maternal education levels as in Table 1. White lines = linear fit with 95% CIs in color, color = corpus. Black lines = 99% CIs of sample means bootstrapped separately from linear fit for each level of SES. These data (as well as our main models and further analyses in SI 3H/G) do not reveal an SES effect on child speech.

Fig. 5.

Sample demographics. Number of daylong recordings (Top row) and children (Bottom row) in the full dataset across demographic variables. For socioeconomic status (SES), $<$H.S. = less than high school degree, H.S. = high school degree, S.U. = some university, B.A. = bachelor’s degree, >B.A. = advanced degree. For child gender, F = female, M = male. For monolingual status (monoling.), Y = monolingual, N = not monolingual. For normative development (norm.), Y = normative, N = nonnormative.