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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., (© 2016 International Society for Autism Research, Wiley Periodicals, Inc.)

The Language ENvironment Analysis Language and Autism Screen (LLAS) is an automated vocal production analysis that has been shown to be a valid screener for autism in hearing children between the ages of 24 to 48 months of age. Although there is reportedly a higher incidence of autism among children who are deaf or hard of hearing, the diagnosis of autism is usually later than that in children with hearing. None of the traditional screening instruments have been used with children with hearing loss. Data about the utility of LLAS with children who are deaf or hard of hearing will be presented and discussed. Though more data will be needed, an LLAS at-risk flag in conjunction with the Social Quotient from the Child Development Inventory holds significant promise for a screen for children who are deaf or hard of hearing., (Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.)

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.

This study investigated whether parent perceptions of their own and their child's levels of talkativeness were related to objective measures recorded via the Language Environment Analysis (LENA) System. Parents of 258 children aged 7 to 60 months completed a questionnaire on which they rated how much they and their child talked. Six months previously, they had recorded in their home language environment using the LENA System. Compared with recording measures, parents tended to overestimate how much they talk to their child, but were somewhat closer when estimating their child's talkativeness. Results were similar for a smaller sample with concurrent recordings, indicating that calibration of talk volubility is challenging without a reference standard. An important implication is that parents' motivation to participate in language-focused interventions may be reduced. That is, parents who overestimate how much they talk to their child may also underestimate what they could do to enhance their child's home language environment.

Changes in natural language environments of families receiving quantitative language feedback in Shanghai were investigated. Volunteer parents of 22 children aged 5 to 30 months were recruited from a hospital and a learning center. Quantitative measures of adult word count and conversational turns with children were collected regularly over 6 months. Feedback reports to caregivers included individual family plus group counts. Impact was assessed by changes in quantitative measures and pre-post child language assessments. Overall, families increased word/turn counts significantly during the first 3 months and then regressed to baseline levels. However, parents whose word count output was below median at baseline significantly increased word count output to study conclusion. Adult word and turn counts were related to a subset of language development measures. Quantitative feedback with parent training had a significant impact on adult-child interactions, particularly for below-median families. Larger studies of wider socioeconomic status with control groups are needed.

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.