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arXiv · 1807.05855

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

Hosung Park·Donghyun Lee·Minkyu Lim·Yoseb Kang·Juneseok Oh·Ji-Hwan Kim
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Abstract

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.

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Hosung Park, Donghyun Lee, Minkyu Lim, Yoseb Kang, Juneseok Oh, Ji-Hwan Kim. 2018-07-11. A Fast-Converged Acoustic Modeling for Korean Speech Recognition: A Preliminary Study on Time Delay Neural Network. https://arxiv.org/abs/1807.05855

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