| A neat thing is this uses a language model reduce the error rate a lot. Autocorrect, conceptually, though theirs is offline rather than as-you-input. In a way the clever software involved is maybe the thing that most makes it relevant to this site. Other tricks we use for noisy touchscreen input could apply here too. For one thing, we've got different models for to collect input. You could try offering autocompletions/predictions to make typing long words faster (long a thing in e.g. single-switch input tools), if watching a screen doesn't slow the person down much. You could try a swipe-like flow where you collect a chunk of data imprecisely but fast (mentally type or scribble a whole word, say), then offer choices. (Thinking about other extremes, I wonder if there's a way at faster input using a T9-like reduced alphabet (3x3 grid, pick row and column?) or something like that. Or if it could someday work to for people to try to speak or visualize words instead of handwrite.) You might be able to glue the letter decoding and language model together more closely--feed the letter-decoding NN's uncertainty and less-probable guesses (25% chance this 'e' was really an 'l') to the lang model. You could learn a person-specific language model, seeding it with a person's writings/speech before the injury or disease (if available), whatever else you think they might need (family/friend names, care requests, etc.), and training the language model as they use the interface (paper already does that with the character-recognizing model). You could do explicit "mutual training"--while the machine samples how you write letters, it can show you its certainty scores (based on the model it has so far) or maybe something graphical to help you write the letters how it expects (exaggerate certain differences etc.). They already have an "optimized alphabet" that maximizes the machine-visible differences between letters. From their paper, the existing language model already did very well at producing clean results in this test, but the more you can refine the cleanup, the more you can potentially sacrifice cleanliness of input for greater speed, and maybe get closer to speaking rate. FWIW some Googling found http://web.stanford.edu/~shenoy/GroupPublications/WillettEtA... |