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Classification of Children with/without Autism Spectrum Disorder Using Speech Signal

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Proceedings of 2023 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2023) (MICAD 2023)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1166))

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Abstract

Autism Spectrum Disorder (ASD) is a neurodevelopmental and neurological disorder related to brain development, leading to problems of social communication and interaction. While there is no cure for ASD, effective and early interventions can improve its symptoms. Hence screening this problem from early ages is very important. In our research, speech of children and machine learning are used to classify children with ASD from typically developing ones. Obtained results show that the combination of speech features and k-Nearest Neighbor model is a promising approach for early detection of ASD.

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References

  1. American Psychiatric Association, Diagnostic and Statistical Manual of Mental Disorders (DSM-5), Fifth Edition, 2013. https://www.psychiatry.org/psychiatrists/practice/dsm. Accessed 22 June 2023

  2. 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

  3. Liu, C., Conn, K., Sarkar, N., Stone, W.: Physiology-based affect recognition for computer-assisted intervention of children with Autism Spectrum Disorder. Int. J. Human-Comput. Stud. 66(9), 662–677 (2008)

    Google Scholar 

  4. 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)

    Google Scholar 

  5. 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)

    Google Scholar 

  6. 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)

    Google Scholar 

  7. Ibrahim, S., Djemal, R., Alsuwailem, A.: Electroencephalography (EEG) signal processing for epilepsy and autism spectrum disorder diagnosis. Biocybern. Biomed. Eng. 38(1), 16–26 ISSN 0208–5216 (2018)

    Google Scholar 

  8. 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)

    Article  Google Scholar 

  9. 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)

    Google Scholar 

  10. 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 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany (2019)

    Google Scholar 

  11. 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)

    Google Scholar 

  12. 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)

    Article  Google Scholar 

  13. Liao, M., Duan H., Wang G.: Application of machine learning techniques to detect the children with autism spectrum disorder. J. Healthc. Eng. 2022, 9340027 (2022)

    Google Scholar 

  14. 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, 101898 (2023)

    Google Scholar 

  15. Carey, M.J., Parris, E.S., Lloyd–Thomas, H.: A comparison of features for speech, music discrimination. In: ICASSP’99, pp. 149 – 152 (1999)

    Google Scholar 

  16. 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)

    Google Scholar 

  17. Hermansky, H.: Perceptual linear predictive (PLP) analysis of speech. JASA 87(4), 1738–1752 (1990)

    Article  Google Scholar 

  18. Bishop, C.M.: Neural Networks for Pattern Recognition, Oxford University Press (1995)

    Google Scholar 

  19. Breiman, L., Friedman, J., Olshen, R., Stone, C.: Classification and Regression Trees, Belmont, USA (1984)

    Google Scholar 

  20. Jang, J.-S.R.: ANFIS: adaptive-network-based fuzzy inference systems. IEEE Trans. Syst. Man Cybern. 23(3), 665–685 (1993)

    Article  Google Scholar 

  21. McLachlan, G., Peel, D.: Finite Mixture Models. John Wiley & Sons Inc, Hoboken, NJ (2000)

    Book  Google Scholar 

  22. DuMouchel, W.H., O'Brien, F.L.: Integrating a robust option into a multiple regression computing environment, computer science and statistics. In: Proceedings of the 21st Symposium on the Interface. Alexandria, VA: American Statistical Association (1989)

    Google Scholar 

  23. Jolliffe, I.: Principal Component Analysis. Springer, New York (1986)

    Book  Google Scholar 

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Correspondence to Nguyen Cong-Phuong .

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Cong-Phuong, N. (2024). Classification of Children with/without Autism Spectrum Disorder Using Speech Signal. In: Su, R., Zhang, YD., Frangi, A.F. (eds) Proceedings of 2023 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2023). MICAD 2023. Lecture Notes in Electrical Engineering, vol 1166. Springer, Singapore. https://doi.org/10.1007/978-981-97-1335-6_20

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  • DOI: https://doi.org/10.1007/978-981-97-1335-6_20

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-1334-9

  • Online ISBN: 978-981-97-1335-6

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