The RERC on AAC is a collaborative center committed to advancing knowledge and producing innovative engineering solutions in augmentative and alternative communication (AAC). The RERC on AAC will support a research and development program that addresses three areas of rehabilitation science and engineering:
- Improving access to technologies through exploration of innovative approaches and through integration of multi-modalities;
- Developing innovative language support technologies, including natural language processing and computer-mediation, to support effective communication for children and adults with limited access to language;
- Improving the human computer interface to reduce cognitive visual processing demands and enhance communication performance.
We also will support a range of training and dissemination activities. Our goal is that the AAC technologies and knowledge generated by the RERC on AAC will enable individuals with complex communication needs to achieve the basic human right of communication, and to maximize their participation in education, employment, health and community activities.
Mandak et al. evaluated the effects of online instruction in a family-centered, relational skills strategy on preservice SLPs use of family-centered skills.
Bhana et al. share how VSDs can be easily implemented during shared storybook readings to support communication and participation.
Caron et al. (2020) investigated the effects of T2L software features within a grid-based AAC app on the single-word reading skills of five individuals with CCN.
A collection of strategies and resources for individuals who rely on AAC and their families during the COVID 19 crisis.
Babb et al. (2020) investigated the use of video VSDs to support participation during a volunteer activity for adolescents with CCN.
David McNaughton provided 3 teleconference presentations to the Sillala AAC conference in Helsinki, Finland.
Özdenizci et al. (2020) introduce an adversarial inference approach to learn representations that are invariant to inter-subject variabilities within a discriminative setting.
Özdenizci & Erdoğmuş (2019) address potential confounders caused by heuristic feature ranking and selection based dimensionality reduction methods that are widely used for brain interfaces and extend this focus with a novel information theoretic feature transformation concept.
McNaughton et al. (2019) article was the most frequently downloaded paper in the AAC journal in 2019.
RERC on AAC to present at the ATIA 2019 Conference in Orlando, FL.