Abstract
People with Autism Spectrum Disorder (ASD) will have problems with social interaction and communication. This may impair their quality of life. Currently there is no medicine for treating ASD. Fortunately, its symptoms may be alleviated if early interventions are applied. Early screening for ASD is therefore essential. Proposed methods in literature used behavioral and physiological signals to deal with this problem. In our research, we used four features extracted from speech signals of three- to six-year children. These were tested with seven classification models. Experiments showed that these features of speech, combined with decision tree, can be employed for early screening of ASD for children with an error rate of 0.25%.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
American Psychiatric Association: Diagnostic and Statistical Manual of Mental Disorders (DSM-5), 5th edn (2013). https://www.psychiatry.org/psychiatrists/practice/dsm. Accessed 22 June 2023
Center for Disease Control and Prevention: Autism Spectrum Disorder (ASD), Data and Statistics. https://www.cdc.gov/ncbddd/autism/data.html. Accessed 30 June 2023
Liu, C., Conn, K., Sarkar, N., Stone, W.: Physiology-based affect recognition for computer-assisted intervention of children with Autism Spectrum Disorder, 66(9), 662–677 (2008)
Bosl, W.J., Tager-Flusberg, H., Nelson, C.A.: EEG analytics for early detection of autism spectrum disorder: a data-driven approach. Sci. Rep. 8(1), 6828 (2018)
Abdulhay, E., Alafeef, M., Alzghoul, L.: Computer-aided autism diagnosis via second-order difference plot area applied to EEG empirical mode decomposition. Neural Comput. Appl. 32, 1–10 (2018)
Emerson, R.W., et al.: Functional neuroimaging of high-risk 6-month-old infants predicts a diagnosis of autism at 24 months of age. Sci. Transl. Med. 9(393) (2017)
Ibrahim, S., Djemal, R., Alsuwailem, A.: Electroencephalography (EEG) signal processing for epilepsy and autism spectrum disorder diagnosis. Biocybern. Biomed. Eng. 38(1), 16–26 (2018). ISSN 0208-5216
Liu, W., Li, M., Yi, L.: Identifying children with autism spectrum disorder based on their face processing abnormality: a machine learning framework. Autism Res. 9(8), 888–898 (2016)
Jaiswal, S., Valstar, M.F., Gillott, A., Daley, D.: Automatic detection of ADHD and ASD from expressive behaviour in RGBD data. In: Proceedings of the IEEE International Conference on Automatic Face Gesture Recognition, Washington, DC, USA (2017)
Jiang, M., Sunday, M.F., Srishyla, D., Conelea, C., Zhao, Q., Jacob, S.: Classifying individuals with ASD through facial emotion recognition and eye-tracking. In: Proceedings of the 41st IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany (2019)
Li, J., Zhong, Y., Ouyang, G.: Identification of ASD children based on video data. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 367–372 (2018)
Tang, C., et al.: Automatic identification of high-risk autism spectrum disorder: a feasibility study using video and audio data under the still-face paradigm. IEEE Trans. Neural Syst. Rehabil. Eng. 28, 2401–2410 (2020)
Liao, M., Duan H., Wang G.: Application of machine learning techniques to detect the children with autism spectrum disorder. J. Healthc. Eng. 2022 (2022)
Khare, S.K., et al.: Application of data fusion for automated detection of children with developmental and mental disorders: a systematic review of the last decade. Inf. Fusion 99 (2023)
Carey, M.J., Parris, E.S., Lloyd–Thomas, H.: A comparison of features for speech, music discrimination. In: ICASSP’99, pp. 149–152 (1999)
McCowan, I., Gatica-Perez, D., Bengio, S., Lathoud, G., Barnard, M., Zhang, D.: Automatic analysis of multimodal group actions in meetings. IEEE Trans. Pattern Anal. Mach. Intell. 27(3), 305–317 (2005)
Hermansky, H.: Perceptual linear predictive (PLP) analysis of speech. JASA 87(4), 1738–1752 (1990)
Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)
Breiman, L., Friedman, J., Olshen, R., Stone, C.: Classification and Regression Trees, Belmont, USA (1984)
Jang, J.-S.R.: ANFIS: adaptive-network-based fuzzy inference systems. IEEE Trans. Syst. Man Cybern. 23(3), 665–685 (1993)
McLachlan, G., Peel, D.: Finite Mixture Models. Wiley, Hoboken (2000)
DuMouchel, W.H., O'Brien, F.L.: Integrating a robust option into a multiple regression computing environment. In: Computer Science and Statistics: Proceedings of the 21st Symposium on the Interface. American Statistical Association, Alexandria, VA (1989)
Jolliffe, I.: Principal Component Analysis. Springer, New York (1986)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Cong-Phuong, N. (2024). Speech Analysis for Autism Spectrum Disorder Detection for Children. In: Rojas, I., Ortuño, F., Rojas, F., Herrera, L.J., Valenzuela, O. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2024. Lecture Notes in Computer Science(), vol 14848. Springer, Cham. https://doi.org/10.1007/978-3-031-64629-4_8
Download citation
DOI: https://doi.org/10.1007/978-3-031-64629-4_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-64628-7
Online ISBN: 978-3-031-64629-4
eBook Packages: Computer ScienceComputer Science (R0)