iSCAN: a phoneme-based predictive communication aid for nonspeaking individuals

The high incidence of literacy deficits among people with severe speech impairments (SSI) has been well documented. Without literacy skills, people with SSI are unable to effectively use orthographic-based communication systems to generate novel linguistic items in spontaneous conversation. To address this problem, phoneme-based communication systems have been proposed which enable users to create spoken output from phoneme sequences. In this paper, we investigate whether prediction techniques can be employed to improve the usability of such systems. We have developed iSCAN, a phoneme-based predictive communication system, which offers phoneme prediction and phoneme-based word prediction. A pilot study with 16 able-bodied participants showed that our predictive methods led to a 108.4% increase in phoneme entry speed and a 79.0% reduction in phoneme error rate. The benefits of the predictive methods were also demonstrated in a case study with a cerebral palsied participant. Moreover, results of a comparative evaluation conducted with the same participant after 16 sessions using iSCAN indicated that our system outperformed an orthographic-based predictive communication device that the participant has used for over 4 years.