Discriminative training of tied mixture density HMMs for online handwritten digit recognition

This paper describes and evaluates the maximum mutual information criterion (MMI) for online unconstrained-style handwritten digit recognition based on hidden Markov models (HMMs). The study focuses on determining the best MMI optimization scheme and the HMM parameters that exhibit the most discriminative capabilities in the context of tied mixture density hidden Markov models (TDHMMs), where all HMM states share a pool of Gaussians. The experimental results show that the second-order optimization scheme is the most efficient and that although means and covariance matrix are shared by all models, they contribute the most to discrimination.