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Summary

This summary is machine-generated.

SpeakFaster, an LLM-powered interface, significantly reduces motor actions for augmentative and alternative communication (AAC) users. This technology enhances text entry rates for individuals with motor impairments, improving quality of life.

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Area of Science:

Assistive Technology
Human-Computer Interaction
Natural Language Processing

Background:

Accelerating text input is crucial for individuals with motor impairments using augmentative and alternative communication (AAC).
Large language models (LLMs) offer new possibilities for improving AAC text entry efficiency.

Purpose of the Study:

To introduce SpeakFaster, an LLM-powered user interface designed to accelerate text entry in AAC.
To evaluate the motor savings and typing speed improvements offered by SpeakFaster compared to traditional methods.

Main Methods:

Developed an LLM-powered user interface (SpeakFaster) utilizing highly-abbreviated text entry.
Conducted offline simulations to assess motor action savings.
Performed a pilot study with 19 non-AAC participants on a mobile device.
Conducted lab and field testing with two eye-gaze AAC users who have amyotrophic lateral sclerosis (ALS).

Main Results:

Offline simulations indicated SpeakFaster saved 57% more motor actions than traditional predictive keyboards.
Pilot study with non-AAC users showed motor savings consistent with simulations and minor changes in typing speed.
AAC users with ALS experienced 29-60% higher text-entry rates compared to baseline, attributed to LLM-driven keystroke savings.

Conclusions:

LLM-assisted text entry, as demonstrated by SpeakFaster, significantly enhances text-entry rates for AAC users, particularly those with severe motor impairments.
The findings support the potential of LLMs to improve AAC efficiency and user quality of life.
SpeakFaster provides a foundation for future research into LLM applications in AAC and other interfaces.