Large margin training for hidden Markov models with partially observed states

Large margin learning of Continuous Density HMMs with a partially labeled dataset has been extensively studied in the speech and handwriting recognition fields. Yet due to the non-convexity of the optimization problem, previous works usually rely on severe approximations so that it is still an open problem. We propose a new learning algorithm that relies on non-convex optimization and bundle methods and allows tackling the original optimization problem as is. It is proved to converge to a solution with accuracy ε with a rate O (1/ε). We provide experimental results gained on speech and handwriting recognition that demonstrate the potential of the method.