Jill K. Dolata

Dr. Jill K. Dolata

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Research Focus & Metrics

Academic Career Length

19Years (2008-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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20 February 2018 in Exceptional Children
Verified
Journal Article
Closed access
JSJames Sinclair
James Sinclair
SHSarah G. Hansen
Sarah G. Hansen
WMWendy Machalicek
Wendy Machalicek
CKChristen Knowles
Christen Knowles
KHKara A. Hirano
Kara A. Hirano
CMChristopher Murray
Christopher Murray
Given the continued changes in demographic diversity of students in the United States, it is important to ensure that participants included in special education research reflect the diversity of the classroom. We examined 16 years of intervention research across 12 special education journals to evaluate the extent to which diverse student populations (e.g., race, ethnicity, disability, sexual orientation, English language learner status) were included in published intervention research. We analyzed 495 intervention articles (9.6%) out of 5,180 total articles. Results revealed that progress has been made in the inclusion of diverse participants in special education intervention research compared with previously conducted reviews, yet some racial and ethnic populations are still underrepresented. We discuss strategies for recruitment and retention of underrepresented diverse populations.

30 May 2014 in Review Journal of Autism and Developmental Disorders
Verified
Journal Article
Closed access
PDF file
SHSarah G. Hansen
Sarah G. Hansen
ABAllison W. Blakely
Allison W. Blakely
JDJill K. Dolata
Jill K. Dolata
TRTracy Raulston
Tracy Raulston
WMWendy Machalicek
Wendy Machalicek

15 September 2019
Verified
Conference Paper
Closed access
RGRobert Gale
Robert Gale
LCLiu Chen
Liu Chen
JDJill K. Dolata
Jill K. Dolata
JSJan van Santen
Jan van Santen
MAMeysam Asgari
Meysam Asgari
This study explores building and improving an automatic speech recognition (ASR) system for children aged 6–9 years and diagnosed with autism spectrum disorder (ASD), language impairment (LI), or both. Working with only 1.5 hours of target data in which children perform the Clinical Evaluation of Language Fundamentals Recalling Sentences task, we apply deep neural network (DNN) weight transfer techniques to adapt a large DNN model trained on the LibriSpeech corpus of adult speech. To begin, we aim to find the best proportional training rates of the DNN layers. Our best configuration yields a 29.38% word error rate (WER). Using this configuration, we explore the effects of quantity and similarity of data augmentation in transfer learning. We augment our training with portions of the OGI Kids’ Corpus, adding 4.6 hours of typically developing speakers aged kindergarten through 3rd grade. We find that 2nd grade data alone — approximately the mean age of the target data — outperforms other grades and all the sets combined. Doubling the data for 1st, 2nd, and 3rd grade, we again compare each grade as well as pairs of grades. We find the combination of 1st and 2nd grade performs best at a 26.21% WER.

30 September 2008 in Infant Behavior and Development
Verified
Journal Article
Closed access
JDJill K. Dolata
Jill K. Dolata
BDBarbara Davis
Barbara Davis
PMPeter F. MacNeilage
Peter F. MacNeilage

11 December 2019 in Maternal and Child Health Journal
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Journal Article
Closed access
PDF file
KAKristi L. Atkins
Kristi L. Atkins
JDJill K. Dolata
Jill K. Dolata
PBPatricia M. Blasco
Patricia M. Blasco
SSSage N. Saxton
Sage N. Saxton
SDSusanne W. Duvall
Susanne W. Duvall

7 April 2022 in Research in Autism Spectrum Disorders
Verified
Journal Article
Closed access
JDJill K. Dolata
Jill K. Dolata
SSShannon Suarez
Shannon Suarez
BCBeth Calamé
Beth Calamé
EFEric Fombonne
Eric Fombonne