Using language cluster models in hierarchical language identification

Hierarchical language identification systems can be employed to take advantage of similarities and disparities between languages to organize them into clusters and decompose the language identification problem into a tree of potentially simpler sub-problems of language group identifications. In this paper, a novel approach is proposed to incorporate knowledge of the language clusters into the front-ends of the classification systems employed in each node of a hierarchical language identification system. This approach investigates the use of feature representations tuned to the particular language cluster identification sub-problem at each node. In addition, we explore a novel decision strategy that incorporates information about language cluster model memberships into the front-ends at each node. Experimental results included in this paper demonstrate that both approaches lead to improved language identification performance of the overall hierarchical system on the NIST LRE 2015 database.