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What Automated Vocal Analysis Reveals About the Vocal Production and Language Learning Environment of Young Children with Autism

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Abstract

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.

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Notes

  1. Participants with an echolalia diagnosis were excluded from the current study because their frequent repetition of words or phrases could inflate the estimates for automatic vocalization counts and conversational turn counts without reflecting advanced language skills or meaningful interactions.

  2. We repeated the analyses presented here matching instead on number of sessions to substantively similar results.

  3. Within-conversation child vocalizations (defined in the LENA Automated Language Measures section) were significantly less variable with respect to duration in the ASD sample (SD = 0.7 s) than in the TD sample (SD = 1.7 s), Levene Test F = 9.02, p < .01. All other comparisons were nonsignificant with p > .05.

  4. See Ford et al. (2008) for detailed information on the DLP hardware and audio recording specifications. Note that the LENA language analysis segmentation process has been simplified here and is described in more detail in Xu et al. (2008b).

  5. See Xu et al. (2008a) for more detail regarding the 70 test set files, and see Gilkerson et al. (2008) for information about inter-rater transcription reliability.

  6. Standardized percentile values for all measures were referenced to the original normative sample from the LENA Foundation Natural Language Study described previously.

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Acknowledgments

The research was funded by the LENA Foundation, Boulder, Colorado. Steven F. Warren and D. Kimbrough Oller are both members of the Scientific Advisory Board of non-profit LENA Foundation. They do not receive consultation fees for serving in this role. Oller’s participation in the preparation of this article was funded by the Plough Foundation. Jill Gilkerson, Jeffery Richards, Dongxin Xu, Umit Yapanel, and Sharmistha Gray are employees of the LENA Foundation. An earlier version of this paper was presented at the annual convention of the Autism Society of America, Orlando, Florida, July 16th, 2008. We gratefully acknowledge Terrance Paul for conceiving of the LENA system and for personally funding and directing its development as well as the development of the LENA Foundation Natural Language Corpus.

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Authors and Affiliations

  1. Institute for Life Span Studies, University of Kansas, Lawrence, KS, 66045-7555, USA

    Steven F. Warren

  2. LENA Foundation, Boulder, CO, USA

    Jill Gilkerson, Jeffrey A. Richards, Dongxin Xu, Umit Yapanel & Sharmistha Gray

  3. The University of Memphis, Memphis, TN, USA

    D. Kimbrough Oller

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  1. Steven F. Warren
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Correspondence to Steven F. Warren.

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Warren, S.F., Gilkerson, J., Richards, J.A. et al. What Automated Vocal Analysis Reveals About the Vocal Production and Language Learning Environment of Young Children with Autism. J Autism Dev Disord 40, 555–569 (2010). https://doi.org/10.1007/s10803-009-0902-5

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