Probabilistic keyboard adaptable to user and operating style based on syllable HMMs

We propose a probabilistic keyboard based on syllable HMMs, as well as an adaptation for users and operating styles to achieve high accuracy on the software keyboard on mobile devices. The syllable HMMs balances high accuracy by introducing syllabic constraints and word flexibility by not depending on a dictionary. Experimental results showed that a user-dependent probabilistic model reduced the error rate by 24.2% compared to the conventional deterministic method. Moreover, we propose to adapt the model to various operating styles using maximum-likelihood linear regression (MLLR). In the experiment, the adaptation was effective with tens of words typed into the style.