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Automated Vocal Analysis of Naturalistic Recordings from Children with Autism, Language Delay, and Typical Development

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Automated Vocal Analysis of Naturalistic Recordings from Children with Autism, Language Delay, and Typical Development Automated vocal analysis of naturalistic recordings from children with autism, language delay, and typical development D. K. Ollera,b,1, P. Niyogic, S. Grayd, J. A. Richardsd, J. Gilkersond,D.Xud, U. Yapaneld, and S. F. Warrene aSchool of Audiology and Speech-Language Pathology, University of Memphis, Memphis, TN 38105; bKonrad Lorenz Institute for Evolution and Cognition Research, Altenberg, Austria A-3422; cDepartments of Computer Science and Statistics, University of Chicago, Chicago, IL 60637; dLENA Foundation, Boulder, CO 80301; and eDepartment of Applied Behavioral Science and Institute for Life Span Studies, University of Kansas, Lawrence, KS 66045 Edited* by E. Anne Cutler, Max Planck Institute for Psycholinguistics, Heilig Landstichting, Netherlands, and approved June 21, 2010 (received for review April 21, 2010) For generations the study of vocal development and its role in disorders. We interpret this as a proof of concept that automated language has been conducted laboriously, with human transcribers analysis can now be included in the tool repertoire for research on and analysts coding and taking measurements from small recorded vocal development and should be expected soon to yield further samples. Our research illustrates a method to obtain measures of scientific benefits with clinical implications. early speech development through automated analysis of massive Naturalistic sampling on the desired scale capitalizes on a battery- quantities of day-long audio recordings collected naturalistically in powered all-day recorder weighing 70 g that can be snapped into ’ children s homes. A primary goal is to provide insights into the de- a chest pocket of children’s clothing, from which it records in velopment of infant control over infrastructural characteristics of completely natural environments (SI Appendix, “Recording De- speech through large-scale statistical analysis of strategically se- vice”). Recordings with this device have been collected since 2006 lected acoustic parameters. In pursuit of this goal we have discov- in three subsamples: typically developing language, language de- ered that the first automated approach we implemented is not only lay, and autism (Fig. 1A and SI Appendix, “Participant Groups, able to track children’s development on acoustic parameters known BIOLOGY Recording and Assessment Procedures”). In both phase I (2006– to play key roles in speech, but also is able to differentiate vocal- SI Appendix, DEVELOPMENTAL izations from typically developing children and children with autism 2008) and phase II (2009) studies ( Table S2), parents or language delay. The method is totally automated, with no human responded to advertisements and indicated if their children had intervention, allowing efficient sampling and analysis at unprece- been diagnosed with autism or language delay. Children in phase I dented scales. The work shows the potential to fundamentally en- with reported diagnosis of language delay were also evaluated by hance research in vocal development and to add a fully objective a speech-language clinician used by our project. Parents of chil- measure to the battery used to detect speech-related disorders in dren with language delay in phase II and parents of children with early childhood. Thus, automated analysis should soon be able to autism in both phases supplied documentation from the di- contribute to screening and diagnosis procedures for early disorders, agnosing clinicians, who were independent of the research. Par- COGNITIVE SCIENCES and more generally, the findings suggest fundamental methods for ent-based assessments obtained concurrently with recordings (SI PSYCHOLOGICAL AND the study of language in natural environments. Appendix, Table S3) confirmed sharp group differences concor- dant with diagnoses; the autism sample had sociopsychiatric vocal development | automated identification of language disorders | profiles similar to those of published autism samples, and low fi all-day recording | automated speaker labeling | autism identi cation language scores occurred in autism and language delay samples (Fig. 1B and SI Appendix, Figs. S2–S4 and “Analyses Indicating ypically developing children in natural environments acquire Appropriate Characteristics of the Participant Groups”). Ta linguistic system of remarkable complexity, a fact that has Demographic information (Fig. 1B and SI Appendix, Tables S3 attracted considerable scientific attention (1–6). Yet research on and S4) was collected through both phases: boys appeared dis- vocal development and related disorders has been hampered by the proportionately in the disordered groups, mother’s educational requirement for human transcribers and analysts to take laborious level (a proxy for socioeconomic status) was higher for children – measurements from small recorded samples (7 11). Consequently, with language disorders, and general developmental levels were fi not only has the scienti c study of vocal development been limited, low in the disordered groups. A total of 113 children were matched but the potential for research to help identify vocal disorders early in life has, with a few exceptions (12–14), been severely restricted. We begin, then, with the question of whether it is possible to Author contributions: D.K.O., P.N., S.G., J.A.R., J.G., D.X., and S.F.W. designed research; construct a fully automated system (i.e., an algorithm) that uses S.G., J.A.R., J.G., D.X., and U.Y. performed research; P.N., S.G., J.A.R., and D.X. contributed nothing but acoustic data from the child’s “natural” linguistic en- new reagents/analytic tools; D.K.O., P.N., S.G., J.A.R., and D.X. analyzed data; and D.K.O., vironment, isolates child vocalizations, extracts significant features, P.N., and S.F.W. wrote the paper. and assesses vocalizations in a useful way for predicting level of Conflict of interest statement: The recordings and hardware/software development were funded by Terrance and Judi Paul, owners of the previous for-profit company Infoture. development and detection of developmental anomalies. There Dissolution of the company was announced February 10, 2009, and it was reconstituted as are major challenges for such a procedure. First, with recordings in the not-for-profit LENA Foundation. All assets of Infoture were given to the LENA Foun- a naturalistic environment, isolating child vocalizations from ex- dation. Before dissolution of the company, D.K.O., P.N., and S.F.W. had received consul- fi traneous acoustic input (e.g., parents, other children, background tation fees for their roles on the Scienti c Advisory Board of Infoture. J.A.R., J.G., and D.X. are current employees of the LENA Foundation. S.G. and U.Y. are affiliates and previous noise, television) is vexingly difficult. Second, even if the child’s employees of Infoture/LENA Foundation. None of the authors has or has had any owner- voice can be isolated, there remains the challenge of determining ship in Infoture or the LENA Foundation. features predictive of linguistic development. We describe proce- *This Direct Submission article had a prearranged editor. dures permitting massive-scale recording and use the data col- Freely available online through the PNAS open access option. lected to show it is possible to track linguistic development with 1To whom correspondence should be addressed. E-mail: [email protected]. totally automated procedures and to differentiate recordings from This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. typically developing children and children with language-related 1073/pnas.1003882107/-/DCSupplemental. www.pnas.org/cgi/doi/10.1073/pnas.1003882107 PNAS Early Edition | 1of6 Downloaded by guest on October 1, 2021 A B C Fig. 1. Demographics. (A) Characteristics of the child groups and recordings. (B) Demographic parameters indicating that groups differed significantly in terms of gender, mother’s education level (a strong indicator of socioeconomic status), and general development age determined by the Child Development Inventory (CDI) (40), an extensive parent questionnaire assessment obtained for 86% of the children, including all those in the matched samples. This CDI age equivalent score is based on 70 items distributed across all of the subscales of the CDI, including language, motor development, and social skills. The groups also differed in age and in scores on the LENA Developmental Snapshot, a 52-item parent questionnaire measure of communication and language, obtained for all participants. (C) Demographic parameters for subsamples matched on gender, mother’s education level, and developmental age. on these variables (Fig. 1C) to allow comparison with results on the The algorithm determined the degree to which each SVI showed entire sample. presence or absence of each of the 12 features, providing a measure Our dataset included 1,486 all-day recordings from 232 children, of infrastructural vocal development on each feature. Each SVI with more than 3.1 million automatically identified child utterances. was classified as plus or minus (i.e., present or absent) for each The signal processing software reliably identified (SI Appendix, feature. The number of “plus” classifications per parameter varied Tables S6 and S7) sequences of utterances from the child wearing from more than 2,400 per recording on the voicing (VC) parameter the recorder, discarded cries and vegetative sounds, and labeled the to fewer than 100 on the wide formant bandwidth parameter (SI remaining consecutive child vocalizations “speech-related
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