Automated Language Environment Analysis: A Research Synthesis

The Predictability of Naturalistic Evaluation of All-Day Recordings for Speech and Language Development

OBJECTIVES

The study aimed to investigate the predictive potential of language environment and vocal development status measures obtained through integrated analysis of Language ENvironment Analysis (LENA) recordings during the prelinguistic stage for subsequent speech and language development in Korean-acquiring children. Specifically, this study explored whether measures from both LENA-automated analysis and human coding at 6-8 months and 12-14 months of age predict vocabulary and phonological development at 18-20 months.

METHOD

One-day home recordings from 20 children were collected using a LENA recorder at 6-8 months, 12-14 months, and 18-20 months. Both LENA-automated measures and measures from human coding were obtained from recordings at 6-8 months and 12-14 months. The number of different words, consonant inventory, and utterance structure inventory were identified from recordings of 18-20 months. Correlation and multiple regression analyses were performed to investigate whether measures related to early language environment and child vocalization at 6-8 months and 12-14 months were predictive of vocabulary and phonological measures at 18-20 months.

RESULTS

The results showed that the two main LENA-automated measures, conversational turn count (CTC) and child vocalization count, were positively correlated with all vocabulary and phonological measures at 18-20 months. Multiple regression analysis revealed that CTC during the prelinguistic stages was the most significant predictor of a number of different words, consonant inventory, and utterance structure inventory at 18-20 months. Also, adult word count in LENA-automated measures, child-directed speech ratio, and canonical babbling ratio measured by human coding significantly predicted some vocabulary and phonological measures at 18-20 months.

CONCLUSION

This study highlights the multifaceted nature of language acquisition and collectively emphasizes the value of considering both quantitative and qualitative aspects of language input to understand early language development in children.

Investigating Children's Interactions in Preschool Classrooms: An Overview of Research Using Automated Sensing Technologies

New technologies that combine digital sensors with automated processing algorithms are now being deployed to study preschool classrooms. This article provides an overview of these new sensing technologies, focusing on automated speaker classification, the analysis of children's and teachers' speech, and the detection and analysis of their movements over the course of the school day. Findings from recent studies utilizing these technologies are presented to illustrate the contribution of these sensing technologies to our understanding of classroom processes that predict children's language and social development. In particular, the potential to collect extended real-time data on the speech and movement of all children and teachers in a classroom provides a broader window on the variability of individual children's interactions with peers and teachers and their integration into classroom social networks. The article describes current challenges related to the use of sensing technologies in preschool settings, as well as advances that may overcome these challenges and allow for more in-depth investigations of children's early classroom experiences.

Progress Toward a Multisectoral Community Intervention Approach to Prevention of the Word Gap

Children learn language through the interactions they have with their parents/caregivers beginning at birth. Hart and Risley (1995) discovered an inequity in the home language input children received from parents/caregivers. Children reared in low-income families received less input (conversations, turns) from parents than did children reared in more advantaged families. Less language input was linked to a disparity in children's vocabulary learning by age 3. The long-term result of this social determinant of early language/literacy learning is a life trajectory of poor educational, economic, and health attainment for many children in families with limited resources, at vast cost to individuals, communities, and the nation. What is needed is an approach to word-gap prevention that is capable of achieving positive individual, community, and population outcomes. Translating research into practice, we developed the Bridging the Word Gap Community Action Planning Guide (BWG-CAPG) using a combined behavior-analytic, community psychology, and public health framework for this purpose (Greenwood et al., 2017). We also developed a progress-monitoring measure, the online BWG Community Check Box Evaluation System, to provide feedback on a community's actions and progress in implementing their plan. Results from an initial pilot investigation within and across three community sectors in a large urban city were promising. BWG Community Check Box results indicated a number of desired outcomes: (a) capacity development and mobilization, (b) community implementation actions, and (c) community changes in practices, programs, and policies. Implications are discussed.

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.

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.

Is silence golden? A pilot study exploring associations between children's language environment and their language skills in Estonian-speaking families

This two-wave longitudinal study explored how Estonian children's language environment relates to their language skills. The Language ENvironment Analysis (LENA) system's automated measures were used as a source of information about children's home language environment. Children's expressive vocabulary was measured via the parent-reported Estonian CDI III (ECDI-III), and language comprehension and production were measured via the examiner-administered New Reynell Developmental Language Scales (NRDLS). The assessments were made 1 year apart at ages 3;0 (years; months) (N = 22) and 4;0 (N = 19). The results revealed wide variability in children's home language environment and language skills. Girls' language production scores were higher; they heard a larger quantity of adult words and spent less time in noisy environments than boys at Wave 2. At Wave 1, children's word count was positively associated with productive language scores, whereas silence was negatively associated with language production and expressive vocabulary. At Wave 2, children who had been more exposed to electronic media scored higher on expressive vocabulary. Distant speech at Wave 1 was positively correlated with language comprehension scores at Wave 2 also when controlling for language comprehension scores at Wave 1. The results, which indicate that distant talk is a positive aspect and silence is a negative aspect of the language environment, highlight the importance of "languagizing" homes also in cultures where silence tends to be more highly valued and talkativeness tends to be less highly valued when compared with English-speaking middle-class families.

A meta-analysis of the predictability of LENA™ automated measures for child language development

Early language environment plays a critical role in child language development. The Language ENvironment Analysis (LENA™) system allows researchers and clinicians to collect daylong recordings and obtain automated measures to characterize a child's language environment. This meta-analysis evaluates the predictability of LENA's automated measures for language skills in young children. We systematically searched reports for associations between LENA's automated measures, specifically, adult word count (AWC), conversational turn count (CTC), and child vocalization count (CVC), and language skills in children younger than 48 months. Using robust variance estimation, we calculated weighted mean effect sizes and conducted moderator analyses exploring the factors that might affect this relationship. The results revealed an overall medium effect size for the correlation between LENA's automated measures and language skills. This relationship was largely consistent regardless of child developmental status, publication status, language assessment modality and method, or the age at which the LENA recording was taken; however, the effect was weakly moderated by the gap between LENA recordings and language measures taken. Among the three measures, there were medium associations between CTC and CVC and language, whereas there was a small-to-medium association between AWC and language. These findings extend beyond validation work conducted by the LENA Research Foundation and suggest certain predictive strength of LENA's automated measures for child language. We discussed possible mechanisms underlying the observed associations, as well as the theoretical, methodological, and clinical implications of these findings.

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.

A Cross-cultural Mixed Methods Investigation of Language Socialization Practices

This embedded mixed methods study explores how cultural differences in language socialization practices influence parent-child verbal interactions. The Language ENvironment Analysis (LENA) System audio recorded families of children who are and are not deaf and hard of hearing in Canada and Vietnam. Software automatically calculated an average conversational turn count. Canadian families participated in more turns than Vietnamese families regardless of hearing status. Interviews with the children's caregivers provided context for these results. Within Vietnamese families, the language socialization practice "Intelligence" results in reduced opportunities for turn-taking, while the Canadian focus on creating personal "Identity" encouraged them. "Intelligence" encompasses Vietnamese participants' desire to ensure their children are learning and "Identity" expresses the Canadian participants' appeal to encourage individuality in their children. The findings suggest directions for the adaptation of intervention. It is the first known study to incorporate LENA results into a mixed methods design.

Measuring Social Communication in the Community: Novel Tools for Advancing Family Participation

Natural contexts and family involvement are key features of effective intervention approaches. However, the measurement of community participation and parent engagement with children remains complex. Therefore, we examined the feasibility of combining use of the Language ENvironment Analysis (LENA^®) system and a global positioning system (GPS; i.e., Qstarz^® BT-Q1000XT). The LENA is a small speech recognition device that captures and quantifies full-day recordings of the natural language environment. The Qstarz BT-Q1000XT is a wearable GPS data logger that allows identification of the locations a person visits. The marrying of these measures allows for an accurate representation of community settings that afford children greater social communication opportunities. Our results show that the combination of measures provides meaningful social communication location data. Also, the participating caregiver reported that the collection of measures was feasible across community settings.

Automated Measures to Understand Communication Opportunities for Young Children With Autism in the Community: A Pilot Study

Community participation is vital to children's development and provides opportunities to practice social communication skills. Although previous studies suggest that young children with autism spectrum disorders (ASD) experience decreased community participation, there is little empirical evidence on the precise patterns of participation that may influence social communication opportunities. Therefore, this pilot study investigated the communication among families of children with ASD ( n = 5) versus typical development (TD; n = 5) across various community locations. We used automated, objective measures: the Language ENvironmental Analysis (LENA) system™ to measure the amount of communication and integrated this with a Global Positioning System (GPS; that is, Qstarz™) to measure community location. Results showed that families of children with ASD and TD spent a similar amount of time in community locations; however, there were differences in the amount of adult talk directed toward children with ASD versus TD across community locations. Findings suggest that automated measures may be successfully integrated to quantify social communication during community participation.