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In this paper, a time delay neural network (TDNN) based acoustic model isproposed to implement a fast-converged acoustic modeling for Korean speechrecognition. The TDNN has an advantage in fast-convergence where the amount oftraining data is limited, due to subsampling which excludes duplicated weights.The TDNN showed an absolute improvement of 2.12% in terms of character errorrate compared to feed forward neural network (FFNN) based modelling for Koreanspeech corpora. The proposed model converged 1.67 times faster than aFFNN-based model did.

A Fast-Converged Acoustic Modeling for Korean Speech Recognition: A Preliminary Study on Time Delay Neural Network