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