A Fast-Converged Acoustic Modeling for Korean Speech Recognition: A Preliminary Study on Time Delay Neural Network
Computation and Language
2018-07-17 v1 Sound
Audio and Speech Processing
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.
Keywords
Cite
@article{arxiv.1807.05855,
title = {A Fast-Converged Acoustic Modeling for Korean Speech Recognition: A Preliminary Study on Time Delay Neural Network},
author = {Hosung Park and Donghyun Lee and Minkyu Lim and Yoseb Kang and Juneseok Oh and Ji-Hwan Kim},
journal= {arXiv preprint arXiv:1807.05855},
year = {2018}
}
Comments
6 pages, 2 figures