ISCA Archive Odyssey 2014
ISCA Archive Odyssey 2014

Robust Language Recognition Based on Diverse Features

Gang Liu, Qian Zhang, John Hansen

In real scenarios, robust language identification (LID) is usually hindered by factors such as background noise, channel, and speech duration mismatches. To address these issues, this study focuses on the advancements of diverse acoustic features, back-ends, and their influence on LID system fusion. There is little research about the selection of complementary features for a multiple system fusion in LID. A set of distinct features are considered, which can be grouped into three categories: classical features, innovative features, and extensional features. In addition, both front-end concatenation and back-end fusion are considered. The results suggest that no single feature type is universally vital across all LID tasks and that a fusion of a diverse set is needed to ensure sustained LID performance in challenging scenarios. Moreover, the back-end fusion also consistently enhances the system performance significantly. To be specifically, the proposed hybrid fusion method benefits system performance with a relative +38.5% and +46.1% improvement on the DARPA RATS and the NIST LRE09 data sets, respectively.

doi: 10.21437/Odyssey.2014-24

Cite as: Liu, G., Zhang, Q., Hansen, J. (2014) Robust Language Recognition Based on Diverse Features. Proc. The Speaker and Language Recognition Workshop (Odyssey 2014), 152-157, doi: 10.21437/Odyssey.2014-24

  author={Gang Liu and Qian Zhang and John Hansen},
  title={{Robust Language Recognition Based on Diverse Features}},
  booktitle={Proc. The Speaker and Language Recognition Workshop (Odyssey 2014)},