Language recognition based on score distribution feature vectors and discriminative classifier fusion - Li, Jinyu (Proc. IEEE Odyssey Workshop on Speaker and Language Recognition, 2006)
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.