Abstract
Diagnoses of autism spectrum disorder (ASD) are difficult due to difference of interviewers and environments, etc. We show relations between utterance features and ASD severity scores, which were manually given by clinical psychologists. These scores are based on the Autism Diagnostic Observation Schedule (ADOS), which is the standard metrics for symptom evaluation for subjects who are suspected as ASD. We built our original corpus where we transcribed voice records of our ADOS evaluation experiment movies. Our corpus is the world largest as speech/dialog of ASD subjects, and there has been no such ADOS corpus available in Japanese language as far as we know. We investigated relationships between ADOS scores (severity) and our utterance features, automatically estimated their scores using support vector regression (SVR). Our average estimation errors were around error rates that human ADOS experts are required not to exceed. Because our detailed analysis for each part of the ADOS test (“puzzle toy assembly + story telling” part and the “depiction of a picture” part) shows different error rates, effectiveness of our features would depend on the contents of the records. Our entire results suggest a new automatic way to assist humans’ diagnosis, which could help supporting language rehabilitation for individuals with ASD in future.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
American Psychiatric Association: Diagnostic and Statistical Manual of Mental Disorders, 5th edn. (DSM-5). American Psychiatric Publishing
American Psychiatric Association: Diagnostic and Statistical Manual of Mental Disorders, 4th edn. Text Revision (DSM-IV-TR). American Psychiatric Association, Philadelphia (2000)
Asgari, M., Bayestehtashk, A., Shafran, I.: Robust and accurate features for detecting and diagnosing autism spectrum disorders. In: Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, pp. 191–194 (2013)
Boyle, C.A, Boulet, S., Schieve, L.A, Cohen, R.A, Blumberg, S.J., Yeargin-Allsopp, M., Visser, S., Kogan, M.D.: Trends in the prevalence of developmental disabilities in US children, 1997–2008. Pediatrics 127(6), 1034–1042 (2011). https://doi.org/10.1542/peds.2010-2989
Fay, W.H.: On the basis of autistic echolalia. J. Commun. Disord. 2(1), 38–47 (1969). https://doi.org/10.1016/0021-9924(69)90053-7
Friedman, L., Lorang, E., Sterling, A.: The use of demonstratives and personal pronouns in fragile X syndrome and autism spectrum disorder. Clin. Linguist. Phon. (2018). https://doi.org/10.1080/02699206.2018.1536727
Gotham, K., Pickles, A., Lord, C.: Standardizing ADOS scores for a measure of severity in autism spectrum disorders. J. Autism Dev. Disord. 39(5), 693–705 (2009). https://doi.org/10.1007/s10803-008-0674-3
Hudenko, W.J., Stone, W., Bachorowski, J.A.: Laughter differs in children with autism: An acoustic analysis of laughs produced by children with and without the disorder. J. Autism Dev. Disord. 39(10), 1392–1400 (2009). https://doi.org/10.1007/s10803-009-0752-1
Kanner, L.: Autistic disturbances of affective contact. Nervous Child (1943). https://doi.org/10.1105/tpc.11.5.949
Kurohashi, S., Kawahara, D.: Japanese morphological analysis system JUMAN 6.0 users manual (2009)
Lord, C., Rutter, M., Lecouteur, A.: Autism diagnostic interview-revised: a revised version of a diagnostic interview for carers of individuals with possible pervasive developmental disorders. Autism Dev. Disord. 24(5), 659–685 (1994)
Lord, C., Risi, S., Lambrecht, L., Cook, E.H., Leventhal, B.L., DiLavore, P.C., Pickles, Rutter, M.: Autism diagnostic observation schedule (ADOS). J. Autism Dev. Disord. (2000). https://doi.org/10.1007/bf02211841
Rouhizadeh, M., Prud’hommeaux, E., van Santen, J., Sproat, R.: Detecting linguistic idiosyncratic interests in autism using distributional semantic models. In: Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality, 46–50. http://www.aclweb.org/anthology/W/W14/W14-3206 (2014)
Tanabe, K., Kurita, T., Nishida, K., Lučić, B., Amić, D., Suzuki, T.: Improvement of carcinogenicity prediction performances based on sensitivity analysis in variable selection of SVM models. SAR QSAR Environ. Res. 24(7), 565–580 (2013). https://doi.org/10.1080/1062936X.2012.762425
Yaneva, V., Ha, L.A., Eraslan, S., Yesilada, Y., Mitkov, R.: Detecting autism based on eye-tracking data from web searching tasks. In: Proceedings of the 18th Web for all Conference on the Internet of Accessible Things, W4A 2018, (di) (2018)
Acknowledgements
This work was supported by JST AIP-PRISM Grant Number JPMJCR18Z7, JST CREST, JSPS KAKENHI, Japan. We would like to express my gratitude to the subjects and Dr. Kaori Matsumoto, the clinical psychologist who carried out ADOS.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Sakishita, M., Ogawa, C., Tsuchiya, K.J., Iwabuchi, T., Kishimoto, T., Kano, Y. (2020). Autism Spectrum Disorder’s Severity Prediction Model Using Utterance Features for Automatic Diagnosis Support. In: Shaban-Nejad, A., Michalowski, M. (eds) Precision Health and Medicine. W3PHAI 2019. Studies in Computational Intelligence, vol 843. Springer, Cham. https://doi.org/10.1007/978-3-030-24409-5_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-24409-5_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-24408-8
Online ISBN: 978-3-030-24409-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)