Sex differences in infant vocalization and the origin of language.

Seeking to discern the earliest sex differences in language-related activities, our focus is vocal activity in the first two years of life, following up on recent research that unexpectedly showed boys produced significantly more speech-like vocalizations (protophones) than girls during the first year of life.We now bring a much larger body of data to bear on the comparison of early sex differences in vocalization, data based on automated analysis of all-day recordings of infants in their homes. The new evidence, like that of the prior study, also suggests boys produce more protophones than girls in the first year and offers additional basis for informed speculation about biological reasons for these differences. More broadly, the work offers a basis for informed speculations about foundations of language that we propose to have evolved in our distant hominin ancestors, foundations also required in early vocal development of modern human infants.

Language Experience in the Second Year of Life and Language Outcomes in Late Childhood.

OBJECTIVES: Quantity of talk and interaction in the home during early childhood is correlated with socioeconomic status (SES) and can be used to predict early language and cognitive outcomes. We tested the effectiveness of automated early language environment estimates for children 2 to 36 months old to predict cognitive and language skills 10 years later and examined effects for specific developmental age periods. METHODS: Daylong audio recordings for 146 infants and toddlers were completed monthly for 6 months, and the total number of daily adult words and adult-child conversational turnswere automatically estimated with Language Environment Analysis software. Follow-up evaluations at 9 to 14 years of age included language and cognitive testing. Language exposure for 3 age groups was assessed: 2 to 17 months, 18 to 24 months, and ≥25 months. Pearson correlations and multiple linear regression analyses were conducted. RESULTS: Conversational turn counts at 18 to 24 months of age accounted for 14% to 27% of the variance in IQ, verbal comprehension, and receptive and/or expressive vocabulary scores 10 years later after controlling for SES. Adult word counts between 18 and 24 months were correlated with language outcomes but were considerably weakened after controlling for SES. CONCLUSIONS: These data support the hypothesis that early talk and interaction, particularly during the relatively narrow developmental window of 18 to 24 months of age, can be used to predict school-age language and cognitive outcomes. With these findings, we underscore the need for effective early intervention programs that support parents in creating an optimal early language learning environment in the home.

Automated Assessment of Child Vocalization Development Using LENA.

Purpose: To produce a novel, efficient measure of children's expressive vocal development on the basis of automatic vocalization assessment (AVA), child vocalizations were automatically identified and extracted from audio recordings using Language Environment Analysis (LENA) System technology. Method: Assessment was based on full-day audio recordings collected in a child's unrestricted, natural language environment. AVA estimates were derived using automatic speech recognition modeling techniques to categorize and quantify the sounds in child vocalizations (e.g., protophones and phonemes). These were expressed as phone and biphone frequencies, reduced to principal components, and inputted to age-based multiple linear regression models to predict independently collected criterion-expressive language scores. From these models, we generated vocal development AVA estimates as age-standardized scores and development age estimates. Result: AVA estimates demonstrated strong statistical reliability and validity when compared with standard criterion expressive language assessments. Conclusions: Automated analysis of child vocalizations extracted from full-day recordings in natural settings offers a novel and efficient means to assess children's expressive vocal development. More research remains to identify specific mechanisms of operation.

Mapping the Early Language Environment Using All-Day Recordings and Automated Analysis.

PURPOSE: This research provided a first-generation standardization of automated language environment estimates, validated these estimates against standard language assessments, and extended on previous research reporting language behavior differences across socioeconomic groups. METHOD: Typically developing children between 2 to 48 months of age completed monthly, daylong recordings in their natural language environments over a span of approximately 6-38 months. The resulting data set contained 3,213 12-hr recordings automatically analyzed by using the Language Environment Analysis (LENA) System to generate estimates of (a) the number of adult words in the child's environment, (b) the amount of caregiver-child interaction, and (c) the frequency of child vocal output. RESULTS: Child vocalization frequency and turn-taking increased with age, whereas adult word counts were age independent after early infancy. Child vocalization and conversational turn estimates predicted 7%-16% of the variance observed in child language assessment scores. Lower socioeconomic status (SES) children produced fewer vocalizations, engaged in fewer adult-child interactions, and were exposed to fewer daily adult words compared with their higher socioeconomic status peers, but within-group variability was high. CONCLUSIONS: The results offer new insight into the landscape of the early language environment, with clinical implications for identification of children at-risk for impoverished language environments.

Conceptualizing a Public Health Prevention Intervention for Bridging the 30 Million Word Gap.

Early childhood experience is a social determinant of children's health and well-being. The well-being of young children is founded on their relationships and interactions with parents and family members in the home, caregivers, and teachers in early education, and friends and families in the greater community. Unfortunately, the early language experience of infants and toddlers from low-income families is typically vastly different than children from middle- and higher-income families. Hart and Risley (Meaningful differences in the everyday experience of young American children. Brookes, Baltimore, 1995) described a "30 Million Word Gap" experienced by age four for children from poor families compared to economically advantaged families as measured by the number of words delivered by adults in the home to their children. This discrepancy between groups is associated with a deficit in vocabulary growth over time (Hart and Risley in Meaningful differences in the everyday experience of young American children. Brookes, Baltimore, 1995; in The social world of children learning to talk. Brookes, Baltimore, MD, 1999; in Am Educ (Spring), 1-9. http://isites.harvard.edu/fs/docs/icb.topic1317532.files/09-10/Hart-Risley-2003.pdf , 2003), and readiness when they enter preschool and kindergarten compared to their more advantaged classmates. The purpose of this paper is to conceptualize a population-level public health prevention approach to research addressing the harmful impacts of the Word Gap. The approach includes use of evidence-based practices to improve children's language environments to foster their early language and literacy learning in early childhood. After a brief review of the Word Gap, we discuss four aspects: a conceptual framework, a community leadership team as driver of the local intervention, evidence-based language interventions for reducing the gap and promoting child language, and the measurements needed. Implications are discussed.

The stability and validity of automated vocal analysis in preverbal preschoolers with autism spectrum disorder.

Theory and research suggest that vocal development predicts "useful speech" in preschoolers with autism spectrum disorder (ASD), but conventional methods for measurement of vocal development are costly and time consuming. This longitudinal correlational study examines the reliability and validity of several automated indices of vocalization development relative to an index derived from human coded, conventional communication samples in a sample of preverbal preschoolers with ASD. Automated indices of vocal development were derived using software that is presently "in development" and/or only available for research purposes and using commercially available Language ENvironment Analysis (LENA) software. Indices of vocal development that could be derived using the software available for research purposes: (a) were highly stable with a single day-long audio recording, (b) predicted future spoken vocabulary to a degree that was nonsignificantly different from the index derived from conventional communication samples, and (c) continued to predict future spoken vocabulary even after controlling for concurrent vocabulary in our sample. The score derived from standard LENA software was similarly stable, but was not significantly correlated with future spoken vocabulary. Findings suggest that automated vocal analysis is a valid and reliable alternative to time intensive and expensive conventional communication samples for measurement of vocal development of preverbal preschoolers with ASD in research and clinical practice. Autism Res 2017, 10: 508-519. © 2016 International Society for Autism Research, Wiley Periodicals, Inc.

Evaluating language environment analysis system performance for Chinese: a pilot study in Shanghai.

PURPOSE: The purpose of this study was to evaluate performance of the Language Environment Analysis (LENA) automated language-analysis system for the Chinese Shanghai dialect and Mandarin (SDM) languages. METHOD: Volunteer parents of 22 children aged 3-23 months were recruited in Shanghai. Families provided daylong in-home audio recordings using LENA. A native speaker listened to 15 min of randomly selected audio samples per family to label speaker regions and provide Chinese character and SDM word counts for adult speakers. LENA segment labeling and counts were compared with rater-based values. RESULTS: LENA demonstrated good sensitivity in identifying adult and child; this sensitivity was comparable to that of American English validation samples. Precision was strong for adults but less so for children. LENA adult word count correlated strongly with both Chinese characters and SDM word counts. LENA conversational turn counts correlated similarly with rater-based counts after the exclusion of three unusual samples. Performance related to some degree to child age. CONCLUSIONS: LENA adult word count and conversational turn provided reasonably accurate estimates for SDM over the age range tested. Theoretical and practical considerations regarding LENA performance in non-English languages are discussed. Despite the pilot nature and other limitations of the study, results are promising for broader cross-linguistic applications.

Automated Vocal Analysis of Children With Hearing Loss and Their Typical and Atypical Peers.

OBJECTIVES: This study investigated automatic assessment of vocal development in children with hearing loss compared with children who are typically developing, have language delays, and have autism spectrum disorder. Statistical models are examined for performance in a classification model and to predict age within the four groups of children. DESIGN: The vocal analysis system analyzed 1913 whole-day, naturalistic acoustic recordings from 273 toddlers and preschoolers comprising children who were typically developing, hard of hearing, language delayed, or autistic. RESULTS: Samples from children who were hard of hearing patterned more similarly to those of typically developing children than to the language delayed or autistic samples. The statistical models were able to classify children from the four groups examined and estimate developmental age based on automated vocal analysis. CONCLUSIONS: This work shows a broad similarity between children with hearing loss and typically developing children, although children with hearing loss show some delay in their production of speech. Automatic acoustic analysis can now be used to quantitatively compare vocal development in children with and without speech-related disorders. The work may serve to better distinguish among various developmental disorders and ultimately contribute to improved intervention.

Automated analysis of child phonetic production using naturalistic recordings.

PURPOSE: Conventional resource-intensive methods for child phonetic development studies are often impractical for sampling and analyzing child vocalizations in sufficient quantity. The purpose of this study was to provide new information on early language development by an automated analysis of child phonetic production using naturalistic recordings. The new approach was evaluated relative to conventional manual transcription methods. Its effectiveness was demonstrated by a case study with 106 children with typical development (TD) ages 8-48 months, 71 children with autism spectrum disorder (ASD) ages 16-48 months, and 49 children with language delay (LD) not related to ASD ages 10-44 months. METHOD: A small digital recorder in the chest pocket of clothing captured full-day natural child vocalizations, which were automatically identified into consonant, vowel, nonspeech, and silence, producing the average count per utterance (ACPU) for consonant and vowel. RESULTS: Clear child utterances were identified with above 72% accuracy. Correlations between machine-estimated and human-transcribed ACPUs were above 0.82. Children with TD produced significantly more consonants and vowels per utterance than did other children. Children with LD produced significantly more consonants but not vowels than did children with ASD. CONCLUSION: The authors provide new information on typical and atypical language development in children with TD, ASD, and LD using an automated computational approach.

A social feedback loop for speech development and its reduction in autism.

We analyzed the microstructure of child-adult interaction during naturalistic, daylong, automatically labeled audio recordings (13,836 hr total) of children (8- to 48-month-olds) with and without autism. We found that an adult was more likely to respond when the child's vocalization was speech related rather than not speech related. In turn, a child's vocalization was more likely to be speech related if the child's previous speech-related vocalization had received an immediate adult response rather than no response. Taken together, these results are consistent with the idea that there is a social feedback loop between child and caregiver that promotes speech development. Although this feedback loop applies in both typical development and autism, children with autism produced proportionally fewer speech-related vocalizations, and the responses they received were less contingent on whether their vocalizations were speech related. We argue that such differences will diminish the strength of the social feedback loop and have cascading effects on speech development over time. Differences related to socioeconomic status are also reported.

Vocal interaction between children with Down syndrome and their parents.

PURPOSE: The purpose of this study was to describe differences in parent input and child vocal behaviors of children with Down syndrome (DS) compared with typically developing (TD) children. The goals were to describe the language learning environments at distinctly different ages in early childhood. METHOD: Nine children with DS and 9 age-matched TD children participated; 4 children in each group were ages 9-11 months, and 5 were between 25 and 54 months. Measures were derived from automated vocal analysis. A digital language processor measured the richness of the child's language environment, including number of adult words, conversational turns, and child vocalizations. RESULTS: Analyses indicated no significant differences in words spoken by parents of younger versus older children with DS and significantly more words spoken by parents of TD children than parents of children with DS. Differences between the DS and TD groups were observed in rates of all vocal behaviors, with no differences noted between the younger versus older children with DS, and the younger TD children did not vocalize significantly more than the younger DS children. CONCLUSIONS: Parents of children with DS continue to provide consistent levels of input across the early language learning years; however, child vocal behaviors remain low after the age of 24 months, suggesting the need for additional and alternative intervention approaches.

Stability and validity of an automated measure of vocal development from day-long samples in children with and without autism spectrum disorder.

Individual difference measures of vocal development may eventually aid our understanding of the variability in spoken language acquisition in children with autism spectrum disorder (ASD). Large samples of child vocalizations may be needed to maximize the stability of vocal development estimates. Day-long vocal samples can now be automatically analyzed based on acoustic characteristics of speech likeness identified in theoretically driven and empirically cross-validated quantitative models of typical vocal development. This report indicates that a single day-long recording can produce a stable estimate for a measure of vocal development that is highly related to expressive spoken language in a group of young children with ASD and in a group that is typically developing. Autism Res 2013, 6: 103-107. © 2013 International Society for Autism Research, Wiley Periodicals, Inc.

Objective child behavior measurement with naturalistic daylong audio recording and its application to autism identification.

Child behavior in the natural environment is a subject that is relevant for many areas of social science and bio-behavioral research. However, its measurement is currently based mainly on subjective approaches such as parent questionnaires or clinical observation. This study demonstrates an objective and unobtrusive child vocal behavior measurement and monitoring approach using daylong audio recordings of children in the natural home environment. Our previous research has shown significant performance in childhood autism identification. However, there remains the question of why it works. In the previous study, the focus was more on the overall performance and data-driven modeling without regard to the meaning of underlying features. Even if a high risk of autism is predicted, specific information about child behavior that could contribute to the automated categorization was not further explored. This study attempts to clarify this issue by exploring the details of underlying features and uncovering additional behavioral information buried within the audio streams. It was found that much child vocal behavior can be measured automatically by applying signal processing and pattern recognition technologies to daylong audio recordings. By combining many such features, the model achieves an overall autism identification accuracy of 94% (N=226). Similar to many emerging non-invasive and telemonitoring technologies in health care, this approach is believed to have great potential in child development research, clinical practice and parenting.

What automated vocal analysis reveals about the vocal production and language learning environment of young children with autism.

The study compared the vocal production and language learning environments of 26 young children with autism spectrum disorder (ASD) to 78 typically developing children using measures derived from automated vocal analysis. A digital language processor and audio-processing algorithms measured the amount of adult words to children and the amount of vocalizations they produced during 12-h recording periods in their natural environments. The results indicated significant differences between typically developing children and children with ASD in the characteristics of conversations, the number of conversational turns, and in child vocalizations that correlated with parent measures of various child characteristics. Automated measurement of the language learning environment of young children with ASD reveals important differences from the environments experienced by typically developing children.

Child vocalization composition as discriminant information for automatic autism detection.

Early identification is crucial for young children with autism to access early intervention. The existing screens require either a parent-report questionnaire and/or direct observation by a trained practitioner. Although an automatic tool would benefit parents, clinicians and children, there is no automatic screening tool in clinical use. This study reports a fully automatic mechanism for autism detection/screening for young children. This is a direct extension of the LENA (Language ENvironment Analysis) system, which utilizes speech signal processing technology to analyze and monitor a child's natural language environment and the vocalizations/speech of the child. It is discovered that child vocalization composition contains rich discriminant information for autism detection. By applying pattern recognition and machine learning approaches to child vocalization composition data, accuracy rates of 85% to 90% in cross-validation tests for autism detection have been achieved at the equal-error-rate (EER) point on a data set with 34 children with autism, 30 language delayed children and 76 typically developing children. Due to its easy and automatic procedure, it is believed that this new tool can serve a significant role in childhood autism screening, especially in regards to population-based or universal screening.

Teaching by listening: the importance of adult-child conversations to language development.

OBJECTIVE: To test the independent association of adult language input, television viewing, and adult-child conversations on language acquisition among infants and toddlers. METHODS: Two hundred seventy-five families of children aged 2 to 48 months who were representative of the US census were enrolled in a cross-sectional study of the home language environment and child language development (phase 1). Of these, a representative sample of 71 families continued for a longitudinal assessment over 18 months (phase 2). In the cross-sectional sample, language development scores were regressed on adult word count, television viewing, and adult-child conversations, controlling for socioeconomic attributes. In the longitudinal sample, phase 2 language development scores were regressed on phase 1 language development, as well as phase 1 adult word count, television viewing, and adult-child conversations, controlling for socioeconomic attributes. RESULTS: In fully adjusted regressions, the effects of adult word count were significant when included alone but were partially mediated by adult-child conversations. Television viewing when included alone was significant and negative but was fully mediated by the inclusion of adult-child conversations. Adult-child conversations were significant when included alone and retained both significance and magnitude when adult word count and television exposure were included. CONCLUSIONS: Television exposure is not independently associated with child language development when adult-child conversations are controlled. Adult-child conversations are robustly associated with healthy language development. Parents should be encouraged not merely to provide language input to their children through reading or storytelling, but also to engage their children in two-sided conversations.

Audible television and decreased adult words, infant vocalizations, and conversational turns: a population-based study.

OBJECTIVE: To test the hypothesis that audible television is associated with decreased parent and child interactions. DESIGN: Prospective, population-based observational study. SETTING: Community. PARTICIPANTS: Three hundred twenty-nine 2- to 48-month-old children. MAIN EXPOSURES: Audible television. Children wore a digital recorder on random days for up to 24 months. A software program incorporating automatic speech-identification technology processed the recorded file to analyze the sounds the children were exposed to and the sounds they made. Conditional linear regression was used to determine the association between audible television and the outcomes of interest. OUTCOME MEASURES: Adult word counts, child vocalizations, and child conversational turns. RESULTS: Each hour of audible television was associated with significant reductions in age-adjusted z scores for child vocalizations (linear regression coefficient, -0.26; 95% confidence interval [CI], -0.29 to -0.22), vocalization duration (linear regression coefficient, -0.24; 95% CI, -0.27 to -0.20), and conversational turns (linear regression coefficient, -0.22; 95% CI, -0.25 to -0.19). There were also significant reductions in adult female (linear regression coefficient, -636; 95% CI, -812 to -460) and adult male (linear regression coefficient, -134; 95% CI, -263 to -5) word count. CONCLUSIONS: Audible television is associated with decreased exposure to discernible human adult speech and decreased child vocalizations. These results may explain the association between infant television exposure and delayed language development.