  The partially observable hidden Markov model is an extension of the hidden
Markov Model in which the hidden state is conditioned on an independent Markov
chain. This structure is motivated by the presence of discrete metadata, such
as an event type, that may partially reveal the hidden state but itself
emanates from a separate process. Such a scenario is encountered in keystroke
dynamics whereby a user's typing behavior is dependent on the text that is
typed. Under the assumption that the user can be in either an active or passive
state of typing, the keyboard key names are event types that partially reveal
the hidden state due to the presence of relatively longer time intervals
between words and sentences than between letters of a word. Using five public
datasets, the proposed model is shown to consistently outperform other anomaly
detectors, including the standard HMM, in biometric identification and
verification tasks and is generally preferred over the HMM in a Monte Carlo
goodness of fit test.
