Abstract
The conversational abilities of people with specific communications requirements are termed as Augmentative and Alternative Communication. It is a hierarchical organizational construction focused on communications usability; appropriateness of communications; and actualization of information, judgment, and capabilities. In particular, Lighter concluded that entities needing AAC to improve and combine their expertise, determination, and abilities in four interrelated areas illustrate communication abilities: verbal, organizational, interpersonal, and political. Knowledge of data extended this concept to claim that communication ability has been affected by various psychological influences and environmental challenges that promote grammar, organizational, interpersonal, tactical skills, and knowledge. With Machine Learning Augmentative and Alternative Communication, an artificial intelligence, voice model communications board aimed to decrease these issues’ influence to mitigate some real problems often faced by AAC users. MLAAC provides customization above and above the traditional object platform and enhances artificial intelligence for smart recommendations. It promotes broader connectivity, with a low cost or local network, easy access to various personal computers. MLAAC is developed for autism and individuals with the trauma that impairs people’s capacity, for example, heart attack autonomic dysfunction, to interact efficiently. Two experiments demonstrated the machine’s significant benefits to MLAAC both restricted and not-restricted users in terms of performance and responsiveness.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Bu, Y., & Small, K. (2018). Active learning in recommendation systems with multi-level user preferences. arXiv preprint.
Jiang, R., Mou, X., Shi, S., Zhou, Y., Wang, Q., Dong, M., & Chen, S. (2020). Object tracking on event cameras with offline–online learning. CAAI Transactions on Intelligence Technology, 5(3), 165–171. https://doi.org/10.1049/trit.2019.0107
Gilroy, S. P., Leader, G., & McCleery, J. P. (2018). A pilot community-based randomized comparison of speech-generating devices and the picture exchange communication system for children diagnosed with an autism spectrum disorder. Autism Research, 11(12), 1701–1711.
Pramanik, S., & Mallick, R. (2020). MULTIMOORA strategy for solving multi-attribute group decision making (MAGDM) in trapezoidal neutrosophic number environment. CAAI Transactions on Intelligence Technology, 5(3), 150–156. https://doi.org/10.1049/trit.2019.0101
Hemsley, B., Bryant, L., Schlosser, R. W., Shane, H. C., Lang, R., Paul, D., Banajee, M., & Ireland, M. (2018). A systematic review of facilitated communication 2014–2018 finds no new evidence that messages delivered using facilitated communication are authored by the person with a disability. Autism & Developmental Language Impairments, 3, 2396941518821570.
Chen, Y., Lin, T., Muthu, B., & Sivaparthipan, C. B. (2020). Study on ethical dilemmas faced by teaching professionals in rural environments. Current Psychology, 1–10.
Poushter, J., Bishop, C., & Chwe, H. (2018). Social media use continues to rise in developing countries but plateaus across developed ones. Pew Research Center, 22, 2–19.
Zhou, J., Yang, J., Song, H., Ahmed, S. H., Mehmood, A., & Lv, H. (2016). An online marking system conducive to learning. Journal of Intelligent & Fuzzy Systems, 31(5), 2463–2471.
Rashid, M., Sulaiman, N., Mustafa, M., Khatun, S., Bari, B. S., & Hasan, M. J. (2020). Recent Trends and Open Challenges in EEG Based Brain-Computer Interface Systems. In InECCE2019 (pp. 367–378). Springer.
Kumar, N., Lee, J. H., Chilamkurti, N., & Vinel, A. (2015). Energy-efficient multimedia data dissemination in vehicular clouds: Stochastic-reward-nets-based coalition game approach. IEEE Systems Journal, 10(2), 847–858.
Liu, M., Wu, W., Gu, Z., Yu, Z., Qi, F., & Li, Y. (2018). Deep learning based on Batch Normalization for P300 signal detection. Neurocomputing, 275, 288–297.
Marín, C. E. M., Lovelle, J. M. C., & Espada, J. P. (2014). A Mechanism of abstraction for independent definition of game’s platform elements. Visión Electrónica, 8(1), 134–142.
Xu, M., Xiao, X., Wang, Y., Qi, H., Jung, T. P., & Ming, D. (2018). A brain-computer interface based on miniature-event-related potentials induced by very small lateral visual stimuli. IEEE Transactions on Biomedical Engineering, 65(5), 1166–1175.
Kumar, G., Saha, R., Buchanan, W. J., Geetha, G., Thomas, R., Rai, M. K., Kim, T. H., & Alazab, M. (2020). Decentralized accessibility of e-commerce products through blockchain technology. Sustainable Cities and Society, 62, 102361.
Aminanto, M. E., Choi, R., Tanuwidjaja, H. C., Yoo, P. D., & Kim, K. (2017). Deep abstraction and weighted feature selection for Wi-Fi impersonation detection. IEEE Transactions on Information Forensics and Security, 13(3), 621–636.
Golpayegani, S. A. H., Esmaeili, L., Mardani, S., & Mutallebi, S. M. (2019). A survey of trust in social commerce. e-systems for the 21st century: concept, developments, and applications-two volume set, 1.
Jiao, Y., Zhang, Y., Wang, Y., Wang, B., Jin, J., & Wang, X. (2018). A novel multilayer correlation maximization model for improving CCA-based frequency recognition in SSVEP brain-computer interface. International Journal of Neural Systems, 28(04), 1750039.
Wu, D., Zhu, H., Zhu, Y., Chang, V., He, C., Hsu, C. H., Wang, H., Feng, S., Tian, L., & Huang, Z. (2020). Anomaly detection based on RBM-LSTM neural network for CPS in advanced driver assistance system. ACM Transactions on Cyber-Physical Systems, 4(3), 1–17.
Koch Fager, S., Fried-Oken, M., Jakobs, T., & Beukelman, D. R. (2019). New and emerging access technologies for adults with complex communication needs and severe motor impairments: State of the science. Augmentative and Alternative Communication, 35(1), 13–25.
Gupta, R., Tanwar, S., Al-Turjman, F., Italiya, P., Nauman, A., & Kim, S. W. (2020). Smart contract privacy protection using AI in cyber-physical systems: Tools, techniques and challenges. IEEE Access, 8, 24746–24772.
Light, J., McNaughton, D., Beukelman, D., Fager, S. K., Fried-Oken, M., Jakobs, T., & Jakobs, E. (2019a). Challenges and opportunities in augmentative and alternative communication: Research and technology development to enhance communication and participation for individuals with complex communication needs. Augmentative and Alternative Communication, 35(1), 1–12.
Al-Turjman, F., & Houdjedj, A. (2019). Learning in Cities’ Cloud-Based IoT. In The Cloud in IoT-enabled Spaces (pp. 209–210). CRC Press.
Light, J., McNaughton, D., & Caron, J. (2019b). New and emerging AAC technology supports for children with complex communication needs and their communication partners: State of the science and future research directions. Augmentative and Alternative Communication, 35(1), 26–41.
Elhoseny, M., Metawa, N., & Hassanien, A. E. (2016, December). An automated information system to ensure quality in higher education institutions. In 2016 12th International Computer Engineering Conference (ICENCO) (pp. 196–201). IEEE.
McNaughton, D., Light, J., Beukelman, D. R., Klein, C., Nieder, D., & Nazareth, G. (2019). Building capacity in AAC: A person-centered approach to supporting participation by people with complex communication needs. Augmentative and Alternative Communication, 35(1), 56–68.
Elhoseny, M., Metawa, N., Darwish, A., & Hassanien, A. E. (2017). Intelligent information system to ensure quality in higher education institutions, towards an automated e-university. International Journal of Computational Intelligence Studies, 6(2–3), 115–149.
Kent-Walsh, J., & Binger, C. (2018). Methodological advances, opportunities, and challenges in AAC research. Augmentative and Alternative Communication, 34(2), 93–103.
Mooney, A., Bedrick, S., Noethe, G., Spaulding, S., & Fried-Oken, M. (2018). Mobile technology to support lexical retrieval during activity retell in primary progressive aphasia. Aphasiology, 32(6), 666–692.
O’Neill, T. A. (2018). Perspectives of parents of children with cerebral palsy on the supports, challenges, and realities of integrating AAC into everyday life.
Brumberg, J. S., Pitt, K. M., Mantie-Kozlowski, A., & Burnison, J. D. (2018). Brain-computer interfaces for augmentative and alternative communication: A tutorial. American Journal of Speech-Language Pathology, 27(1), 1–12.
von Tetzchner, S. (2018). Introduction to the special issue on aided language processes, development, and use: An international perspective. Augmentative and Alternative Communication, 34(1), 1–15.
Wobbrock, J. O., Gajos, K. Z., Kane, S. K., & Vanderheiden, G. C. (2018). Ability-based design. Communications of the ACM, 61(6), 62–71.
King, M., Romski, M., & Sevcik, R. A. (2020). Growing up with AAC in the digital age: A longitudinal profile of communication across contexts from toddler to teen. Augmentative and Alternative Communication, 36(2), 128–141.
Gevarter, C., & Horan, K. (2019). A behavioral intervention package to increase vocalizations of individuals with autism during speech-generating device intervention. Journal of Behavioral Education, 28(1), 141–167.
Wendt, O., Hsu, N., Simon, K., Dienhart, A., & Cain, L. (2019). Effects of an iPad-based speech-generating device infused into instruction with the picture exchange communication system for adolescents and young adults with a severe autism spectrum disorder. Behavior Modification, 43(6), 898–932.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Li, W., Qiu, X., Li, Y. et al. Towards a novel machine learning approach to support augmentative and alternative communication (AAC). Int J Speech Technol 25, 331–341 (2022). https://doi.org/10.1007/s10772-021-09903-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10772-021-09903-2