Socioeconomic status correlates with measures of Language Environment Analysis (LENA) system: a meta-analysis

Using a meta-analytic approach, we evaluate the association between socioeconomic status (SES) and children's experiences measured with the Language Environment Analysis (LENA) system. Our final analysis included 22 independent samples, representing data from 1583 children. A model controlling for LENATM measures, age and publication type revealed an effect size of r z = .186, indicating a small effect of SES on children's language experiences. The type of LENA metric measured emerged as a significant moderator, indicating stronger effects for adult word counts than child vocalization counts. These results provide important evidence for the strength of association between SES and children's everyday language experiences as measured with an unobtrusive recording analyzed automatically in a standardized fashion.

Evaluating the Language ENvironment Analysis System for Korean

Purpose The algorithm of the Language ENvironment Analysis (LENA) system for calculating language environment measures was trained on American English; thus, its validity with other languages cannot be assumed. This article evaluates the accuracy of the LENA system applied to Korean. Method We sampled sixty 5-min recording clips involving 38 key children aged 7–18 months from a larger data set. We establish the identification error rate, precision, and recall of LENA classification compared to human coders. We then examine the correlation between standard LENA measures of adult word count, child vocalization count, and conversational turn count and human counts of the same measures. Results Our identification error rate (64% or 67%), including false alarm, confusion, and misses, was similar to the rate found in Cristia, Lavechin, et al. (2020) . The correlation between LENA and human counts for adult word count ( r = .78 or .79) was similar to that found in the other studies, but the same measure for child vocalization count ( r = .34–.47) was lower than the value in Cristia, Lavechin, et al., though it fell within ranges found in other non-European languages. The correlation between LENA and human conversational turn count was not high ( r = .36–.47), similar to the findings in other studies. Conclusions LENA technology is similarly reliable for Korean language environments as it is for other non-English language environments. Factors affecting the accuracy of diarization include speakers' pitch, duration of utterances, age, and the presence of noise and electronic sounds.

Validation of the Language ENvironment Analysis (LENA) system for Dutch

The validity of the Language ENvironment Analysis (LENA) System was evaluated for Dutch. 216 5-min samples (six samples per age per child) were selected from daylong recordings at 5, 10 and 14 months of age of native Dutch-speaking younger siblings of children with autism spectrum disorder (N = 6) and of typically developing children (N = 6). Two native Dutch-speaking coders counted the amount of adult words (AWC), child vocalisations (CVC) and conversational turns (CT). Consequently, correlations between LENA and human estimates were explored. Correlations were high for AWC at all ages (r = .73 to .81). Regarding CVC, estimates were moderately correlated at 5 months (r = .57) but the correlation decreased at 10 (r = .37) and 14 months (r = .14). Correlations for CT were low at all ages (r = .19 to .28). Lastly, correlations were not influenced by the risk status of the children.

Natural Language Input: Maternal Education, Socioeconomic Deprivation, and Language Outcomes in Typically Developing Children

Purpose The current study was designed to investigate the differences in language input related to family factors (maternal level of education [MLE] and socioeconomic level of deprivation [SLD]) and their association with language outcomes in preschoolers. Method This study used New Zealand SLD and MLE classification systems to examine differences in language input related to these factors among 20 typically developing preschool children aged 2–5 years. The quantity of children's language input (adult words [AWs], conversational turns [CTs]) was calculated using the Language ENvironment Analysis audiotaping technology for two typical weekend days. Four 5-min Language ENvironment Analysis recording segments were transcribed and coded, and parental language strategies were classified as optimal language strategy, moderate language strategy, or sub-optimal language strategy (S-OLS) for child language outcomes. The receptive and expressive language of each child was assessed using the Preschool Language Scales–Fifth Edition. Results Mann–Whitney U tests showed significant differences between the quantity of language input (AWs/hr, CTs/hr) for high and low MLE and high and low SLD groups. Consistent with the literature, the use of S-OLSs was significantly lower for families with high MLE ( Mdn = .25, IQR = .14) and low SLD ( Mdn = .22, IQR = .13) than for families with low MLE ( Mdn = .41, IQR = .24) and high SLD ( Mdn = .41, IQR = .26). Spearman correlation coefficients indicated significant associations between language input (AWs/hr, CTs/hr, S-OLSs) and language outcomes. Conclusions Reduced language input and the frequent use of S-OLSs associated with low maternal education and high deprivation and low language outcomes for these children highlight the importance for all parents/families to learn optimal language strategies to support the development of strong language skills in their children in young age.

Accuracy of the Language Environment Analyses (LENATM) system for estimating child and adult speech in laboratory settings

Laboratory observations are a mainstay of language development research, but transcription is costly. We test whether speech recognition technology originally designed for day-long contexts can be usefully applied to this use-case. We compared automated adult word and child vocalization counts from Language Environment Analysis (LENATM) to those of transcribers in 20-minute play sessions with Spanish-speaking dyads (n = 104) at 1;7 and 2;2. For adult words, results indicated moderate associations but large absolute differences. Associations for child vocalizations were weaker with larger absolute discrepancies. LENA has moderate potential to ease the burden of transcription in some research and clinical applications.

Exploring Early Childhood Language Environments: A Comparison of Language Use, Exposure, and Interactions in the Home and Childcare Settings

Purpose This study was conducted in a large Midwestern metropolitan area to examine the language environments at home and in center-based childcare for young children who are living in poverty. We compared child language use and exposure in the home and childcare settings using extended observations with automated Language Environment Analysis to gain a deeper understanding of the environmental factors that may affect change in language outcomes for young children. Method Thirty-eight children, along with parents ( n = 38) and childcare providers ( n = 14) across five childcare centers, participated in this study. Each child completed a standardized language assessment and two daylong recordings with Language Environment Analysis to determine the number of adult words, conversational turns, and child vocalizations that occurred in each setting. Data were analyzed at 5-min intervals across each recording. Results Comparisons between home recordings in this sample and a comparison group showed reliably higher rates of adult words and conversational turns in the home setting. Linear mixed-effects regression models showed significant differences in the child language environments, with the home setting providing higher levels of language input and use. These effects were still meaningful after accounting for the time of day, participant demographic characteristics, and child language ability. Conclusions Practical implications for supporting child language development across settings are discussed, and suggestions for further research are provided. Supplemental Material https://doi.org/10.23641/asha.12042678