LANGUAGE EXPOSURE PREDICTS CHILDREN'S PHONETIC PATTERNING: EVIDENCE FROM LANGUAGE SHIFT

Abstract

Although understanding the role of the environment is central to language acquisition theory, rarely has this been studied for children's phonetic development, and receptive and expressive language experiences in the environment are not distinguished. This last distinction may be crucial for child speech production in particular, because production requires coordination of low-level speech-motor planning with high-level linguistic knowledge. In this study, the role of the environment is evaluated in a novel way-by studying phonetic development in a bilingual community undergoing rapid language shift. This sociolinguistic context provides a naturalistic gradient of the amount of children's exposure to two languages and the ratio of expressive to receptive experiences. A large-scale child language corpus encompassing over 500 hours of naturalistic South Bolivian Quechua and Spanish speech was efficiently annotated for children's and their caregivers' bilingual language use. These estimates were correlated with children's patterns in a series of speech production tasks. The role of the environment varied by outcome: children's expressive language experience best predicted their performance on a coarticulation-morphology measure, while their receptive experience predicted performance on a lower-level measure of vowel variability. Overall these bilingual exposure effects suggest a pathway for children's role in language change whereby language shift can result in different learning outcomes within a single speech community. Appropriate ways to model language exposure in development are discussed.

Keywords: Quechua; Spanish; field phonetics; first language acquisition; language shift; morphology; speech production.

Figure A1.

Clip annotation category counts for each child. Numbers on barplot reflect the number of clips from each category

Figure 1.

Daylong recording collection materials.

Figure 2.

Audio clip generation, selection, and annotation workflow.

Figure 3.

Example area plot of language proportions by number of clips annotated. Area plots were used to track progress toward language proportion stability during daylong recording annotation.

Figure 4.

Example plot of Spanish language proportion variance by number of clips annotated. Variance was computed over a moving window of sixty clips. This plot was used to track progress toward variance stability during daylong recording annotation.

Figure 5.

Proportion of language categories, by maternal language profile. Numbers on barplot reflect percentages of each category. Note: one family did not report maternal language profile.

Figure 6.

Proportion of language categories, by child age (years). Numbers on barplot reflect percentages of each category.

Figure 7.

Children’s vowel spaces by maternal language profile. Ellipses represent 95% CIs, or approximately 2 SDs of all data, assuming a normal t-distribution. Individual points represent a random subset of eight tokens per vowel category.

Figure 8.

Vowel category dispersion by percentage of Spanish clips containing target child. Each point represents one child. Ribbons represent 95% confidence intervals.

Figure 9.

Coarticulation difference by maternal language profile and biphone sequence: there was no reliable effect of profile on coarticulation. Circles and triangles represent each child’s coarticulatory difference across -man ‘allative’ and -pi ‘locative’ morpheme boundaries, respectively. Boxplot hinges represent the interquartile range. Points are jittered horizontally to avoid overlap.

Figure 10.

Coarticulation difference by percentage of Spanish clips containing the target child. Data points represent each child’s average coarticulatory difference by word environment for -man ‘allative’ (blue; dashed line) and -pi ‘locative’ (yellow; solid line). Ribbons represent 95% confidence intervals.

Figure 11.

Coarticulation difference by percentage of Spanish clips containing an adult or nontarget child. Data points represent each child’s average coarticulatory difference by word environment for -man ‘allative’ (blue; dashed line) and -pi ‘locative’ (yellow; solid line). Ribbons represent 95% confidence intervals.

Figure 12.

Mediation model predicting children’s coarticulation difference between word environments. Solid lines denote direct relationships and the dotted line an indirect relationship. The effect of Spanish language input on coarticulation is mediated by the children’s own Spanish language use.