|Gregory Gelly, Jean-Luc Gauvain, Lori Lamel, Antoine Laurent, Viet Bac Le, Abdel Messaoudi|
This paper describes a language recognition system designed to discriminate closely related languages and dialects of the same language. The system was jointly developed by LIMSI and Vocapia Research for the NIST 2015 Language Recognition Evaluation (LRE). The language recognition system results from a fusion of four core classifiers: a phonotactic component using DNN acoustic models, two purely acoustic components using a RNN model and and I-vector model, and a lexical component. Each component generates language posterior probabilities optimized to maximize the LID NCE, thereby making their combination trivial and robust. The motivation for using multiple components representing different speech knowledge is that some dialect distinctions may not be manifest at the acoustic level. We report experiments on the NIST LRE15 data and provide an analysis of the results and some post-evaluation contrasts. The 2015 LRE task focused on the identification of 20 languages clustered in 6 groups (Arabic, Chinese, English, French, Slavic and Iberic) of similar languages. Results are reported using the reference Cavg metric which served as the primary evaluation metric by NIST as well as the EER and LER.