Version 1
: Received: 28 February 2019 / Approved: 4 March 2019 / Online: 4 March 2019 (10:14:44 CET)
How to cite:
Elsahar, Y.; Hu, S.; Bouazza-Marouf, K.; Kerr, D.; Mansor, A. Augmentative and Alternative Communication (AAC) Advances: A Review of Configurations for Speech Disabled Individuals. Preprints2019, 2019030033. https://doi.org/10.20944/preprints201903.0033.v1
Elsahar, Y.; Hu, S.; Bouazza-Marouf, K.; Kerr, D.; Mansor, A. Augmentative and Alternative Communication (AAC) Advances: A Review of Configurations for Speech Disabled Individuals. Preprints 2019, 2019030033. https://doi.org/10.20944/preprints201903.0033.v1
Elsahar, Y.; Hu, S.; Bouazza-Marouf, K.; Kerr, D.; Mansor, A. Augmentative and Alternative Communication (AAC) Advances: A Review of Configurations for Speech Disabled Individuals. Preprints2019, 2019030033. https://doi.org/10.20944/preprints201903.0033.v1
APA Style
Elsahar, Y., Hu, S., Bouazza-Marouf, K., Kerr, D., & Mansor, A. (2019). Augmentative and Alternative Communication (AAC) Advances: A Review of Configurations for Speech Disabled Individuals. Preprints. https://doi.org/10.20944/preprints201903.0033.v1
Chicago/Turabian Style
Elsahar, Y., David Kerr and Annysa Mansor. 2019 "Augmentative and Alternative Communication (AAC) Advances: A Review of Configurations for Speech Disabled Individuals" Preprints. https://doi.org/10.20944/preprints201903.0033.v1
Abstract
High-tech augmentative and alternative communication (AAC) methods are on a constant rise; however, the interaction between the user and the assistive technology is still challenged for an optimal user experience centered around the desired activity. This review presents a range of signal sensing and acquisition methods utilized in conjunction with the existing high-tech AAC platforms for speech disabled individuals, including imaging methods, touch-enabled systems, mechanical and electro-mechanical access, breath-activated methods, and brain computer interfaces (BCI). The listed AAC sensing modalities are compared in terms of ease of access, affordability, complexity, portability, and typical conversational speeds. A revelation of the associated AAC signal processing, encoding, and retrieval highlights the roles of machine learning (ML) and deep learning (DL) in the development of intelligent AAC solutions. The demands and the affordability of most systems were found to hinder the scale of usage of high-tech AAC. Further research is indeed needed for the development of intelligent AAC applications reducing the associated costs and enhancing the portability of the solutions for a real user’s environment. The consolidation of natural language processing with current solutions also needs to be further explored for the amelioration of the conversational speeds. The recommendations for prospective advances in coming high-tech AAC are addressed in terms of developments to support mobile health communicative applications.
Keywords
augmentative and alternative communication; assistive technologies; sensing modalities; signal processing; voice communication; machine learning; mobile health; speech disability
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
