Gaussian Backend design for open-set language detection

This paper proposes a new approach to the challenging open-set language detection task. Most state-of-the-art approaches make use of data sources with several out-of-set languages to model such languages. In the proposed approach, no additional data from out-ofset languages is required, only date from the target languages is used. Experiments are conducted using the LRE-05 and the LRE-07 evaluation data sets with the 30s condition. A Cavg of 4.5% and 3.4% is obtained on these data set, respectively. These results are comparable with other reported results.

• Type: Conference
• Year: 2009
• Where: ICASSP
• Authors: Mohamed Faouzi BenZeghiba, Jean-Luc Gauvain, Lori Lamel
• Keywords: Challenging Open-set Language, Data Set, ICASSP 2009, Most State-of-the-art Approaches, Signal Processing