This paper describes the system submitted by Politecnico di Torino for the 2007 NIST Language Recognition Evaluation. The system, which was among the best participants in this evaluation, is a combination of classifiers based on three acoustic models and on two sets of Parallel Phone tokenizers. It exploits several state-of-the-art techniques that have been successfully applied in recent years both in speaker and in language recognition. We illustrate the models, the classification techniques and the performance of the system components, and of their combination, in the NIST-07 close-set 30 sec General Language Recognition task. We also highlight the difficulties in setting appropriate decision thresholds whenever the training data of a language are scarce, or the test data are collected through previously unseen channels.
Bibliographic reference. Castaldo, Fabio / Dalmasso, Emanuele / Laface, Pietro / Colibro, Daniele / Vair, Claudio (2008): "Politecnico di Torino system for the 2007 NIST language recognition evaluation", In INTERSPEECH-2008, 297-300.