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 individuals with a speech disability, 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 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.
Highlights
Recent studies show that up to 1% of the world population suffers a degree of speech, language or communication need (SLCN) [1,2]
The aim of this paper is to review the access and processing techniques pertaining to predominant high-tech alternative communication (AAC) methods, including the input signal sources, and the developments of machine learning (ML) and deep learning (DL) associated with AAC solutions for the provision of a personalized user experience
A global view of predominant high-tech AAC systems is presented in relation to their signal sensing categories, including the modalities’ key features and sensing mechanisms
Summary
Recent studies show that up to 1% of the world population suffers a degree of speech, language or communication need (SLCN) [1,2]. Augmentative and alternative communication (AAC) incorporates a wide range of processes that augment, complement, or replace speech of individuals with complex communication needs [3,4]. In the broad context of speech and language, speech is often associated with the motor movements responsible for the production of spoken words, whereas language is associated with the cognitive processing skills of communication. AAC solutions are classified into three categories: no-tech, low-tech, and high-tech AAC [4]. No-tech AAC is considered the oldest of the three AAC categories, given its reliance on the interpretation of facial expressions and voluntary motor movements, such as sign language, to deliver non-verbal messages [5]. Low-tech AAC utilizes basic tools, such as books and display boards
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