title: Huffman and linear scanning methods with statistical language models.
abstract: Current scanning access methods for text generation in AAC devices are limited to relatively few options, most notably row/column variations within a matrix. We present Huffman scanning, a new method for applying statistical language models to binary-switch, static-grid typing AAC interfaces, and compare it to other scanning options under a variety of conditions. We present results for 16 adults without disabilities and one 36-year-old man with locked-in syndrome who presents with complex communication needs and uses AAC scanning devices for writing. Huffman scanning with a statistical language model yielded significant typing speedups for the 16 participants without disabilities versus any of the other methods tested, including two row/column scanning methods. A similar pattern of results was found with the individual with locked-in syndrome. Interestingly, faster typing speeds were obtained with Huffman scanning using a more leisurely scan rate than relatively fast individually calibrated scan rates. Overall, the results reported here demonstrate great promise for the usability of Huffman scanning as a faster alternative to row/column scanning.
authors: Google Research , Portland, Oregon USA.
authors: Brian
authors: B
authors: Roark
authors: Melanie
authors: M
authors: Fried-Oken
authors: Chris
authors: C
authors: Gibbons
mesh_terms: D000328:Adult; D020526:Brain Stem Infarctions / Q000534:rehabilitation*; D003143:Communication Devices for People with Disabilities; D006801:Humans; D007802:Language; D008297:Male; D015233:Models, Statistical; D009323:Natural Language Processing; D012984:Software
keywords: Augmentative and alternative communication (AAC); Natural language processing; Scanning