RESUMO
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.
Assuntos
Desenvolvimento Infantil , Reconhecimento Automatizado de Padrão , Interface para o Reconhecimento da Fala , Fala , Criança , Pré-Escolar , Feminino , Humanos , Lactente , Modelos Lineares , Aprendizado de Máquina , Masculino , Reconhecimento Automatizado de Padrão/métodos , Análise de Componente Principal , Reprodutibilidade dos Testes , VozRESUMO
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.