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Published on: July 31, 2016
Artificial Intelligence for Enhancing Emotion Expression in Augmentative and Alternative Communication Systems: A
Kevin M Pitt1, Ciara Ousley1, Christopher Gibbons2
1Department of Special Education & Communication Disorders, University of Nebraska-Lincoln.
Purpose:
Artificial intelligence is increasingly being explored in augmentative and alternative communication (AAC) to support personalized and responsive communication. Integrating emotion recognition into AAC systems may help users convey emotion content in real time, enhancing social interaction and communication quality.
Method:
This preliminary study utilized DeepFace, a facial emotion recognition tool, as a starting point for integrating emotion recognition into AAC systems. Twelve neurotypical children and four children with autism, aged 8-13 years, completed two tasks: an imitated (copy) emotion condition using static images and a semispontaneous emotion-elicitation condition. Similar to existing AAC devices, each participant completed an individualized calibration procedure to help tailor the model to their facial expressions. Number scale ratings of the user experience were also collected.
Results:
Overall, the system demonstrated promising provisional accuracy, particularly for happiness, sadness, and calm (neutrality), although performance varied across emotions, participants, and task types. User experience ratings were generally positive, suggesting satisfaction and engagement with the system, with variability observed.
Conclusions:
Findings highlight the value of calibration and personalization in AAC. Future work should expand training data sets to better represent children's facial expressions, incorporate multimodal inputs, and explore the alignment of synthetic speech output with detected emotions. Broader studies including active AAC users, diverse developmental and cultural backgrounds, and more spontaneous real-world interactions are needed. These results support the preliminary utility of DeepFace as a possible starting point for integrating emotion recognition into AAC systems, but further research is warranted. Directions for user-centered development are also discussed.
Supplemental Material:
https://doi.org/10.23641/asha.32511465.
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