Language identification with Dynamic Hidden Markov network

In this paper, we describe new language identification system based on the recently developed Dynamic Hidden Markov network (DHMnet). The DHMnet is a never-ending learning system and provides high resolution model of the speech space. Speech patterns are represented by paths through the network, and these paths when properly labeled with language IDs provide efficient means to discriminate between languages. First experiments indicated that our system can work on-line and is able to deliver relatively high performance with low latency. Evaluated on three language (English, Japanese and Chinese) identification task, the system achieved identification rates of 87.3% and 89.3% for 3 and 5 seconds long speech segments respectively.