Huffman and linear scanning methods with statistical language models

Brian Roark, Melanie Fried-Oken, Chris Gibbons

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)37-50
Number of pages14
JournalAAC: Augmentative and Alternative Communication
Issue number1
StatePublished - Mar 1 2015


  • Augmentative and alternative communication (AAC)
  • Natural language processing
  • Scanning

ASJC Scopus subject areas

  • Rehabilitation
  • Speech and Hearing


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