Skip to main content

AI and Global AAC Symbol Communication

  • 1276 Accesses

Part of the Lecture Notes in Computer Science book series (LNISA,volume 12376)


Artificial Intelligence (AI) applications are usually built on large trained data models that can recognize and label images, provide speech output from text, process natural language for translation, and be of assistance to many individuals via the internet. For those who are non-verbal or have complex speech and language difficulties, AI has the potential to offer enhanced access to the wider world of communication that can be personalized to suit user needs. Examples include pictographic symbols to augment or provide an alternative to spoken language. However, when using AI models, data related to the use of freely available symbol sets is scarce. Moreover, the manipulation of the data available is difficult with limited annotation, making semantic and syntactic predictions and classification a challenge in multilingual situations. Harmonization between symbol sets has been hard to achieve; this paper aims to illustrate how AI can be used to improve the situation. The goal is to provide an improved automated mapping system between various symbol sets, with the potential to enhance access to more culturally sensitive multilingual symbols. Ultimately, it is hoped that the results can be used for better context sensitive symbol to text or text to symbol translations for speech generating devices and web content.


  • Alternative and augmentative communication
  • Web accessibility
  • Complex communication needs
  • AI and inclusion

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-58796-3_8
  • Chapter length: 8 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
USD   84.99
Price excludes VAT (USA)
  • ISBN: 978-3-030-58796-3
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   109.99
Price excludes VAT (USA)
Fig. 1.


  1. 1.

  2. 2.

  3. 3.

  4. 4.

  5. 5.

  6. 6.

  7. 7.

  8. 8.

  9. 9.

  10. 10.

  11. 11.


  1. Beukelman, D.R., Mirenda, P.: Augmentative & Alternative Communication: Supporting Children and Adults with Complex Communication Needs. Paul H. Brookes Publishing, Baltimore (2013)

    Google Scholar 

  2. Dudy, S., Bedrick, S.: Compositional language modeling for icon-based augmentative and alternative communication. In: Proceedings of the Workshop on Deep Learning Approaches for Low-Resource NLP, pp. 25–32 (2018)

    Google Scholar 

  3. Garay-Vitoria, N., Abascal, J.: Text prediction systems: a survey. Univ. Access Inf. Soc. 4(3), 188–203 (2006)

    CrossRef  Google Scholar 

  4. Higginbotham, D.J., Lesher, G.W., Moulton, B.J., Roark, B.: The application of natural language processing to augmentative and alternative communication. Assistive Technol. 24(1), 14–24 (2012)

    CrossRef  Google Scholar 

  5. Lundälv, M., Derbring, S.: AAC vocabulary standardisation and harmonisation. In: Miesenberger, K., Karshmer, A., Penaz, P., Zagler, W. (eds.) ICCHP 2012. LNCS, vol. 7383, pp. 303–310. Springer, Heidelberg (2012).

    CrossRef  Google Scholar 

  6. Odom, S.L., Horner, R.H., Snell, M.E.: Handbook of Developmental Disabilities. Guilford press, New York (2009)

    Google Scholar 

  7. Sennott, S.C., Akagi, L., Lee, M., Rhodes, A.: AAC and artificial intelligence (AI). Top. Lang. Disord. 39(4), 389–403 (2019)

    CrossRef  Google Scholar 

  8. Speer, R., Chin, J., Havasi, C.: Conceptnet 5.5: an open multilingual graph of general knowledge. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)

    Google Scholar 

  9. Vertanen, K., Kristensson, P.O.: The imagination of crowds: conversational AAC language modeling using crowdsourcing and large data sources. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 700–711. Association for Computational Linguistics (2011)

    Google Scholar 

  10. Waller, A.: Telling tales: unlocking the potential of AAC technologies. Int. J. Lang. Commun. Disord. 54(2), 159–169 (2019)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Chaohai Ding .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Ding, C., Draffan, E.A., Wald, M. (2020). AI and Global AAC Symbol Communication. In: Miesenberger, K., Manduchi, R., Covarrubias Rodriguez, M., Peňáz, P. (eds) Computers Helping People with Special Needs. ICCHP 2020. Lecture Notes in Computer Science(), vol 12376. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58795-6

  • Online ISBN: 978-3-030-58796-3

  • eBook Packages: Computer ScienceComputer Science (R0)