Tied-state multi-path HMnet model using three-domain successive state splitting

In this paper, we address the improvement of an acoustic model using the multi-path Hidden Markov network (HMnet) model for automatically creating non-uniform tied-state, context-dependent hidden markov model topologies. Recent research has achieved multi-path model topologies in order to improve the recognition performance in gender-independent, spontaneous-speaking applications. However, the multi-path acoustic model size may increase and require more training samples depending on the increased number of paths. To solve this problem, we used a tied-state multi-path topology by which we can create a three-domain successive state splitting method to which environmental splitting is added. This method can obtain a suitable model topology with small mixture components. Experiments demonstrated that the proposed multi-path HMnet model performs better than single-path models for the same number of states.