A hybrid phonotactic language identification system with an SVM back-end for simultaneous lecture translation

In this paper we describe our work in constructing a language identification system for use in our simultaneous lecture translation system. We first built PPR and PPRLM baseline systems that produce score-fusing language cue feature vectors for language discrimination and utilize an SVM back-end classifier for the actual language identification. On our bi-lingual lecture tasks the PPRLM system clearly outperforms the PPR system in various segment length conditions, however at the cost of slower run-time. By using lexical information in the form of keyword spotting, and additional language models we show ways to improve the performance of both baseline systems. In order to combine the faster run-time of the PPR system with the better performance of the PPRLM system we finally built a hybrid of both approaches that clearly outperforms the PPR system while not adding any additional computing time. This hybrid system is therefore our choice for the use in the lecture translation system due to its faster run-time and good performance.