Language recognition based on score distribution feature vectors and discriminative classifier fusion

Jinyu Li, Sibel Yaman, and et. al

Abstract

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.

Details

Publication typeInproceedings
Published inProc. IEEE Odyssey Workshop on Speaker and Language Recognition
> Publications > Language recognition based on score distribution feature vectors and discriminative classifier fusion