Confidence measure based language identification

In this paper we present a new application for confidence measures in spoken language processing. In today’s computerized dialogue systems, language identification (LID) is typically achieved via dedicated modules. In our approach, LID is integrated into the speech recognizer, therefore profiting from high-level linguistic knowledge at very little extra cost. Our new approach is based on a word lattice based confidence measure [3], which was originally devised for unsupervised training. In this work, we show that the confidence based language identification algorithm outperforms conventional score based methods. Also, this method is less dependent on the acoustic characteristics of the transmission channel than score based methods. By introducing additional parameters, unknown languages can be rejected. The proposed method is compared to a score based approach on the Verbmobil database, a three language task.