Towards a novel machine learning approach to support augmentative and alternative communication (AAC)

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.