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Maximum mutual information estimation via second order cone programming for large vocabulary continuous speech recognition

In this paper, we have successfully extended our previous work of convex optimization methods to MMIE-based discriminative training for large vocabulary continuous speech recognition. Specifically, we have re-formulated the MMIE training into a second order cone programming (SOCP) program using some convex relaxation techniques that we have previously proposed. Moreover, the entire SOCP formulation has been developed for word graphs instead of N-best lists to handle large vocabulary tasks. The proposed method has been evaluated in the standard WSJ-5k task and experimental results show that the proposed SOCP method significantly outperforms the conventional EBW method in terms of recognition accuracy as well as convergence behavior. Our experiments also show that the proposed SOCP method is efficient enough to handle some relatively large HMM sets normally used in large vocabulary tasks.