A deep learning assisted web application tool for diagnosing communication disorder in children

Research Article
Open access

A deep learning assisted web application tool for diagnosing communication disorder in children

C. S. KanimozhiSelvi 1 , S. Santhiya 2* , Sharmila C 3 , Gayathri S. 4 , Venkatesh V. 5 , Snehaa S. 6
  • 1 Kongu Engineering College    
  • 2 Kongu Engineering College    
  • 3 Kongu Engineering College    
  • 4 Kongu Engineering College    
  • 5 Kongu Engineering College    
  • 6 Kongu Engineering College    
  • *corresponding author santhiya123cse@gmail.com
Published on 14 June 2023 | https://doi.org/10.54254/2755-2721/6/20230721
ACE Vol.6
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-59-1
ISBN (Online): 978-1-915371-60-7

Abstract

Communication is an important way of expressing one's thoughts to others. Many people are suffering from communication disorders like stammering and stuttering. Communicative disorders are mainly divided into three types namely language disorders, speech production disorders, and oral motor/swallowing/feeding disorders. Identifying the problem earlier and giving intervention at the right time is very important for children to improve their personal and academic lives. It will be better for the children to recover from the disorder if the problem is identified earlier. Disorder identification is a complex decision-making process that requires expertise in the medical field. The proposed framework gets the voice input through a web application. Later, deep learning technology predicts the speech disorder of the recorded voice. The deep learning-assisted tool developed in the proposed framework is to identify if the person has a stuttering problem, dysarthria, or whether the person can talk normally. Hence, the proposed method alleviates the problem of identifying speech disorder types by recording the person's voice through a web application and it achieves the accuracy of 95%.

Keywords:

machine learning, deep learning, web application, communication disorder, convolutional neural network.

KanimozhiSelvi,C.S.;Santhiya,S.;C,S.;S.,G.;V.,V.;S.,S. (2023). A deep learning assisted web application tool for diagnosing communication disorder in children. Applied and Computational Engineering,6,1309-1315.
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References

[1]. Witsawakiti, N., Suchato, A., & Punyabukkana, P. (2006). Thai language e-training for the hard of hearing. Special Issue of the International Journal of the Computer, the Internet and Management, 14(SP1).

[2]. Konstantinidis, E. I., Hitoglou-Antoniadou, M., Luneski, A., Bamidis, P. D., & Nikolaidou, M. M. (2009). Using effective avatars and rich multimedia content for the education of children with autism. In Proceedings of the 2nd international conference on pervasive technologies related to assistive environments (pp. 1-6).

[3]. Zhang, M., Wang, X., Sathishkumar VE., & Sivakumar, V (2021). Machine learning techniques based on security management in smart cities using robots. Work, 68(3), 891-902.

[4]. E.I. Toki and J. Pange, (2010) "E-learning activities for articulation in speech-language therapy and learning for preschool children," Procedia-Social and Behavioral Sciences, 2(2), 4274-4278.

[5]. Kaur, A., & Padmanabhan, J. (2017). Children with specific learning disorder: identification and interventions. Educational Quest, 8(1), 1.

[6]. Subramanian, M., Kumar, M. S., Sathishkumar, V. E., Prabhu, J., Karthick, A., Ganesh, S. S., & Meem, M. A. (2022). Diagnosis of retinal diseases based on Bayesian optimization deep learning network using optical coherence tomography images. Computational Intelligence and Neuroscience, 2022.

[7]. Pavithra, E., Janakiramaiah, B., Narasimha Prasad, L. V., Deepa, D., Jayapandian, N., & Sathishkumar, V. E. (2022). Visiting Indian Hospitals Before, During and After COVID. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems.

[8]. Hetzroni, O. E., & Tannous, J. (2004). Effects of a computer-based intervention program on the communicative functions of children with autism. Journal of autism and developmental disorders, 34(2), 95-113.

[9]. Kanimozhiselvi, C. S., & Santhiya, S. (2021, February). Communication Disorder Identification from Recorded Speech using Machine Learning Assisted Mobile Application. In 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV) (pp. 789-793). IEEE.

[10]. Ramdoss, S., Lang, R., Mulloy, A., Franco, J., O’Reilly, M., Didden, R., & Lancioni, G. (2011). Use of computer-based interventions to teach communication skills to children with autism spectrum disorders: A systematic review. Journal of Behavioral Education, 20(1), 55-76.

[11]. Liu, Y., Sathishkumar, V. E., & Manickam, A. (2022). Augmented reality technology based on school physical education training. Computers and Electrical Engineering, 99, 107807.

[12]. J. Pinborough-Zimmerman, R. Satterfield, J. Miller, D. Bilder, S. Hossain, and W. McMahon, [2007] “Communication disorders: Prevalence and comorbid intellectual disability, autism, and emotional/behavioral disorders,” American Journal of Speech-Language Pathology

[13]. Subramanian, M., Sathishkumar, V. E., Ramya, C., Kogilavani, S. V., & Deepti, R. (2022, May). A Lightweight Depthwise Separable Convolution Neural Network for Screening Covid-19 Infection from Chest CT and X-ray Images. In 2022 18th International Conference on Distributed Computing in Sensor Systems (DCOSS) (pp. 410-413). IEEE.

Cite this article

KanimozhiSelvi,C.S.;Santhiya,S.;C,S.;S.,G.;V.,V.;S.,S. (2023). A deep learning assisted web application tool for diagnosing communication disorder in children. Applied and Computational Engineering,6,1309-1315.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

ISBN：978-1-915371-59-1(Print) / 978-1-915371-60-7(Online)
Editor：Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.6
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Witsawakiti, N., Suchato, A., & Punyabukkana, P. (2006). Thai language e-training for the hard of hearing. Special Issue of the International Journal of the Computer, the Internet and Management, 14(SP1).

[2]. Konstantinidis, E. I., Hitoglou-Antoniadou, M., Luneski, A., Bamidis, P. D., & Nikolaidou, M. M. (2009). Using effective avatars and rich multimedia content for the education of children with autism. In Proceedings of the 2nd international conference on pervasive technologies related to assistive environments (pp. 1-6).

[3]. Zhang, M., Wang, X., Sathishkumar VE., & Sivakumar, V (2021). Machine learning techniques based on security management in smart cities using robots. Work, 68(3), 891-902.

[4]. E.I. Toki and J. Pange, (2010) "E-learning activities for articulation in speech-language therapy and learning for preschool children," Procedia-Social and Behavioral Sciences, 2(2), 4274-4278.

[5]. Kaur, A., & Padmanabhan, J. (2017). Children with specific learning disorder: identification and interventions. Educational Quest, 8(1), 1.

[6]. Subramanian, M., Kumar, M. S., Sathishkumar, V. E., Prabhu, J., Karthick, A., Ganesh, S. S., & Meem, M. A. (2022). Diagnosis of retinal diseases based on Bayesian optimization deep learning network using optical coherence tomography images. Computational Intelligence and Neuroscience, 2022.

[7]. Pavithra, E., Janakiramaiah, B., Narasimha Prasad, L. V., Deepa, D., Jayapandian, N., & Sathishkumar, V. E. (2022). Visiting Indian Hospitals Before, During and After COVID. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems.

[8]. Hetzroni, O. E., & Tannous, J. (2004). Effects of a computer-based intervention program on the communicative functions of children with autism. Journal of autism and developmental disorders, 34(2), 95-113.

[9]. Kanimozhiselvi, C. S., & Santhiya, S. (2021, February). Communication Disorder Identification from Recorded Speech using Machine Learning Assisted Mobile Application. In 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV) (pp. 789-793). IEEE.

[10]. Ramdoss, S., Lang, R., Mulloy, A., Franco, J., O’Reilly, M., Didden, R., & Lancioni, G. (2011). Use of computer-based interventions to teach communication skills to children with autism spectrum disorders: A systematic review. Journal of Behavioral Education, 20(1), 55-76.

[11]. Liu, Y., Sathishkumar, V. E., & Manickam, A. (2022). Augmented reality technology based on school physical education training. Computers and Electrical Engineering, 99, 107807.

[12]. J. Pinborough-Zimmerman, R. Satterfield, J. Miller, D. Bilder, S. Hossain, and W. McMahon, [2007] “Communication disorders: Prevalence and comorbid intellectual disability, autism, and emotional/behavioral disorders,” American Journal of Speech-Language Pathology

[13]. Subramanian, M., Sathishkumar, V. E., Ramya, C., Kogilavani, S. V., & Deepti, R. (2022, May). A Lightweight Depthwise Separable Convolution Neural Network for Screening Covid-19 Infection from Chest CT and X-ray Images. In 2022 18th International Conference on Distributed Computing in Sensor Systems (DCOSS) (pp. 410-413). IEEE.

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