The Robustness Study of Multiple Kernel Learning Approaches for VAD
Authors
Jie Zhang, Mantao Wang, Haitao Tang, Qiang Huang, Haibo Pu, Lixin Luo, Zhihao Zhou
Corresponding Author
Mantao Wang
Available Online December 2018.
- DOI
- 10.2991/meici-18.2018.150How to use a DOI?
- Keywords
- Voice activity detection; Deep learning; Multiple Kernel Learning; Robustness
- Abstract
Recently, although the MKL-SVM-based VAD has achieved desirable performance, the VAD base on deep learning networks, are attracting greater research interest than their with overwhelming advantages. In this paper, we focus on investigation and analysis the noise robustness of VAD systems multiple-feature-based on MKL-SVM comparing DBN, LSTM and CNN at frame level under various noisy conditions on TIMIT. Experimental results have shown that the MKL-SVM-based VAD not only is not inferior to deep learning networks VADs, but also has a low detection complexity. Further experiment on the information robustness task demonstrates that the MKL-SVM-based VAD apply the advantages of multiple features effectively.
- Copyright
- © 2018, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Jie Zhang AU - Mantao Wang AU - Haitao Tang AU - Qiang Huang AU - Haibo Pu AU - Lixin Luo AU - Zhihao Zhou PY - 2018/12 DA - 2018/12 TI - The Robustness Study of Multiple Kernel Learning Approaches for VAD BT - Proceedings of the 2018 8th International Conference on Management, Education and Information (MEICI 2018) PB - Atlantis Press SP - 757 EP - 763 SN - 1951-6851 UR - https://doi.org/10.2991/meici-18.2018.150 DO - 10.2991/meici-18.2018.150 ID - Zhang2018/12 ER -