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A Fast-Converged Acoustic Modeling for Korean Speech Recognition: A Preliminary Study on Time Delay Neural Network
July 11, 2018 ยท Declared Dead ยท ๐ arXiv.org
"No code URL or promise found in abstract"
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Authors
Hosung Park, Donghyun Lee, Minkyu Lim, Yoseb Kang, Juneseok Oh, Ji-Hwan Kim
arXiv ID
1807.05855
Category
cs.CL: Computation & Language
Cross-listed
cs.SD,
eess.AS
Citations
2
Venue
arXiv.org
Last Checked
5 months ago
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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