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On using Parameterized Multi-channel Non-causal Wiener Filter-Adapted Convolutional Neural Networks for Distant Speech Recognition

Title
On using Parameterized Multi-channel Non-causal Wiener Filter-Adapted Convolutional Neural Networks for Distant Speech Recognition
Author
장준혁
Keywords
beamforming; Convolutional neural networks; distant speech recognition; PMWF
Issue Date
2016-01
Publisher
IEEE, IEIE
Citation
International Conference on Electronics, Information, and Communications, ICEIC 2016, Article number 7562963, Page. 1-4
Abstract
Recently, the convolutional neural network (CNN) with multiple microphones was proposed to use the delay-sum (DS) beamformer for distant speech recognition (DSR) and compared to the direct use of multiple acoustic channels as a parallel input to the CNN [1]. We explore the parameterized multi-channel non-causal Wiener filter (PMWF) as the front-end to train the CNN, which is applied to acoustic modeling for DSR. For this, we first present a concise description of the basic PMWF as well as its advantages and then explain how to organize the PMWF into the CNN with a novel architecture. Experimental results on the TIMIT dataset show that the proposed PMWF-based CNN approach outperforms the cross-channel CNN and the DS beamformer when evaluating the word error rate (WER) in various DSR environments. © 2016 IEEE.
URI
http://ieeexplore.ieee.org/document/7562963/http://hdl.handle.net/20.500.11754/30389
ISSN
978-1-4673-8016-4
DOI
10.1109/ELINFOCOM.2016.7562963
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > ELECTRONIC ENGINEERING(융합전자공학부) > Articles
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