We present the GT-IIR language recognition system submitted
to the 2005 NIST Language Recognition Evaluation. Different
from conventional frame-based feature extraction, our system
adopts a collection of broad output scores from different
language recognition systems to form utterance-level score
distribution feature vectors over all competing languages, and
build vector-based spoken language recognizers by fusing two
distinct verifiers, one based on a simple linear discriminant
function (LDF) and the other on a complex artificial neural
network (ANN), to make final language recognition decisions.
The diverse error patterns exhibited in individual LDF and
ANN systems facilitate smaller overall verification errors in the
combined system than those obtained in separate systems.