Discriminative training of stochastic Markov graphs for speech recognition

This paper proposes the application of discriminative training techniques based on the generalized probabilistic descent (GPD) approach to stochastic Markov graphs (SMGs), a generalization of mixture-state hidden Markov models (HMMs), describing the constraints in the acoustic structure of speech as a graph consisting of nodes, each containing a base function, and a transition network between the nodes. State-specific weights modeling the classification relevance of the corresponding states and a transition weight representing the ratio between transitions and emissions are trained in addition to the standard parameters of the models. The experiments show, that discriminatively trained SMGs outperform discriminatively trained mixture-state HMMs with approximately the same number of parameters.