Does Technology Speak for Us; Exploring Emotional Range and User Interface Design in Augmentative and Alternative Communication systems (AAC)
Bibliographic record
Abstract
One of the fundamentals of being human is the ability to communicate. We express our thoughts, emotions, and ideas in both subtle and direct forms of communication. Either through movements in body language and eye contact, or verbal expressions in our pitch and tone of voice. Both ways allow us to be a part of a larger society and have a feeling of self-identity and control. When this ability to communicate is taken away due to illness, accident, or for any other unprecedented reason, we are left feeling isolated, disassociated from our identity, and excluded from society. Without verbal communication, expressing a basic need can become taxing and near impossible. Let alone trying to express specific emotions or ideas. There is a multitude of neurological diseases that cause speech impairments such as ALS, Parkinson’s disease, Myasthenia gravis, multiple sclerosis, and more. (Mayo Clinic, 2020) Many of these diseases, like ALS for example, effect over 30,000 people in the United States with 5,000 new documented cases being diagnosed every year (Heller, 2019). There is a large population of people living with these disabilities globally that impair their quality of life. The purpose of this project is to explore the technologies available in speech synthesis and to create an emotionally driven assistive speech solution through a mobile application that allows the user to first select the specific emotion they are feeling, followed by the text or phrase they wish to say. The result is that the text or phrase would be said in that specified emotion by the synthetic voice associated with the application. The main focus of the project will be changing the way augmentative and alternative communication systems are approached by designing an emotionally driven interface that allows the user to choose their emotion before the spoken action/phrase is said out loud by the software. Researching and understanding the fundamentals of the Circumplex Model of Affect will guide the design of the interface and allow for a deeper understanding of human emotions (Russell, 1980). This project will be centered around the design process and more specifically, inclusive design.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".