Atypical or impaired language is one of the core features of autism spectrum disorder (ASD). Yet, what the precise characteristics of language are in ASD and how they differ from those in other disorders such as Specific Language Impairment (SLI) is still substantially unknown. An important obstacle for the study of language in any disorder is that conventional structured instruments (i.e., instruments consisting of a sequence of items, each eliciting a - typically brief - response, such as the Clinical Evaluation of Language Fundamentals [CELF]) may not provide adequate breadth of information: Analysis of natural language samples is required The proposed research will build on recent progress in "Natural Language Processing" (NLP) technology, an area of Computer Science concerned with computational analysis of text. The goal of the proposed research is to develop and validate new NLP based methods that automatically measure language characteristics of ASD based on raw (i.e., not coded) transcripts of natural language samples. The objective is to improve the analysis of natural language samples by enhancing efficiency, reliability, and richness of information extracted. Data on three groups of children ages four to eight will be analyzed, obtained from an earlier study: ASD, SLI, and typically developing children. If successful, the new methods will have important impacts on research and clinical practice for ASD and for other disorders in which language is affected, by enabling analysis of more representative and ecologically valid natural language samples as well as by creating opportunities for discovery of currently unknown language characteristics of ASD by the effortless extraction of numerous language features.

Public Health Relevance

Although atypical or impaired language is one of the core features of autism spectrum disorder (ASD), what the precise characteristics of language are in ASD and how they differ from those in other disorders such as Specific Language Impairment (SLI) is still substantially unknown, in part due to the paucity of instruments for the analysis of natural language samples. The goal of this project is to develop and apply Natural Language Processing technologies to automatically extract ASD-specific language characteristics from uncoded, raw transcripts of natural language samples.

Agency
National Institute of Health (NIH)
Institute
National Institute on Deafness and Other Communication Disorders (NIDCD)
Type
Research Project (R01)
Project #
5R01DC012033-02
Application #
8320877
Study Section
Special Emphasis Panel (ZRG1-AARR-F (52))
Program Officer
Cooper, Judith
Project Start
2011-09-01
Project End
2016-08-31
Budget Start
2012-09-01
Budget End
2013-08-31
Support Year
2
Fiscal Year
2012
Total Cost
$738,723
Indirect Cost
$259,415
Name
Oregon Health and Science University
Department
Engineering (All Types)
Type
Schools of Medicine
DUNS #
096997515
City
Portland
State
OR
Country
United States
Zip Code
97239
van Santen, Jan P H; Sproat, Richard W; Hill, Alison Presmanes (2013) Quantifying repetitive speech in autism spectrum disorders and language impairment. Autism Res 6:372-83