Automatic model complexity control using marginalized discriminative growth functions

Designing a large vocabulary speech recognition system is a highly complex problem. Many techniques affect both the system complexity and recognition performance. Automatic complexity control criteria are needed to quickly predict the recognition performance ranking of systems with varying complexity, in order to select an optimal model structure with the minimum word error. In this paper a novel complexity control technique is proposed by using the marginalization of discriminative growth functions. A two stage approach is adopted to make the marginalization efficient. First a lower bound, related to the auxiliary function, is used to remove the dependence on the latent variables. Second a Laplace approximation is used for the integration. Experimental results on a spontaneous speech recognition task show that marginalized the MMI growth function outperforms data likelihood and standard Bayesian schemes in terms of both recognition performance ranking error and word error.