This paper describes the Loquendo - Politecnico di Torino system evaluated on the 2006 NIST speaker recognition evaluation dataset. This system was among the best participants in this evaluation. It combines the results of two independent GMM systems: a Phonetic GMM and a classical GMM. Both systems rely on an intersession variation compensation approach, performed in the feature domain. It allowed a 30% error rate reduction with respect to our 2005 system. The linear combination of the two GMM engines gives a further 10% error rate reduction.
We also report the results of a set of post evaluation experiments, related to the training data for the intersession variation evaluation, both for the telephone and microphone datasets. The approach adopted for the two wire tests is also described, showing the effect of the speaker segmentation component of our system. Finally, we describe how we performed the incremental unsupervised adaptation tests.
Bibliographic reference. Vair, Claudio / Colibro, Daniele / Castaldo, Fabio / Dalmasso, Emanuele / Laface, Pietro (2007): "Loquendo - Politecnico di torino's 2006 NIST speaker recognition evaluation system", In INTERSPEECH-2007, 1238-1241.