Discriminative Vector for Spoken Language Recognition

Bin Ma, Rong Tong, Haizhou Li

Abstract: We propose a language recognition system based on discriminative vectors, in which parallel phone recognizers serve as the voice tokenization front-end followed by vector space modeling that effectively vectorizes phonotactic features, and the final classification is carried out based on the discriminative vectors. We design an ensemble of discriminative binary classifiers. The output values of these classifiers construct a discriminative vector, also referred to as output codes, to represent the high-dimensional phonotactic features. We achieve equal-error-rate of 1.95%, 3.02% and 4.9% on 1996, 2003 and 2005 NIST LRE databases, respectively, for 30-second trials.