Similar to having an acoustic model as a statistical representation of the phoneme sequence generating the observed acoustic features, a haptic model is used to model the PLI sequence generating the observed haptic inputs, H. The haptic likelihood can be factorised as where  However, it is also possible to have a non-diagonal matrix for p(h i |l i ) in order to accommodate typing errors, so that non-zero probabilities are assigned to cases where For handwriting input, h i denote a sequence of 2dimensional feature vectors, which can be modelled using Hidden Markov Models (HMMs) (Rabiner, 1989).
(h i |l i ) is simply given by the HMM likelihood.