Parallel Algorithm for a Hidden Markov Model with an Indefinite Number of States and Heterogeneous Observation Data

In addition to being a modern technique used in speech recognition applications, Hidden Markov Models (HMMs) are widely used in other areas to predict equipment life cycles and optimize maintenance, for example. Problems of this type have a very limited and fragmented set of observable data, as well as limited information on the possible states of the system. This article proposes a strategy for organizing HMM parallel learning, which is effectively implemented using OpenCL on GPU devices. The originality of this approach lies in the parallel implementation of the learning algorithm for a model with an indefinite number of states and heterogeneous observed data: sometimes only the observed signal is available, and sometimes the state of the system is known. The code presented in this article are parallelized on several GPU devices.