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- W2392673190 abstract "Maximum mutual information based training produces models with better discriminant power. With the complexity of the objective function in this training, gradient based methods were adopted in traditional training. And this has led to the local optimality of the models. This paper proposed a hybrid maximum mutual information/evolutionary computation (MMI/EC) architecture to be embedded in the training of HMMs. Each individual in evolutionary computation represents a set of HMMs, while the fitness value of each individual represents the maximum mutual information. Since evolutionary computation is noted for its global search and population based operations, the globally optimal solutions, or the sub optimal solutions can be obtained. The experimental results indicate that the system trained with the proposed method is superior to the one trained with the traditional gradient based training method." @default.
- W2392673190 created "2016-06-24" @default.
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- W2392673190 date "2002-01-01" @default.
- W2392673190 modified "2023-09-25" @default.
- W2392673190 title "A Hybrid Maximum Mutual Information/Evoluationary Computation Architecture for Speech Recognition" @default.
- W2392673190 hasPublicationYear "2002" @default.
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