Complementary Combination in i-Vector Level for Language Recognition
Recently, i-vector based technology can provide good performance in language recognition (LRE). From the viewpoint of information theory, i-vectors derived from different acoustic features can contain more useful and complementary language information. In this paper, we propose an effective complementary combination method for i-vectors, which derived from two different complementary acoustic features respectively: the popular short-term spectral shifted delta cepstral (SDC) and new spectro-temporal time-frequency cepstrum (TFC). In order to reduce the high dimension of new combined i-vectors and to remove the redundant information, principal component analysis (PCA) and linear discriminant analysis (LDA) are used respectively and the performances are evaluated. Moreover, two popular classifiers including cosine distance scoring (CDS) and support vector machine (SVM) are applied to model the combined low-dimensional i-vectors. The experiments are performed on the NIST LRE 2009 dataset, and the results show that the proposed combination method can effectively provide the better performance with lower dimension. The performance of the best system show that the EER can reduce 1% than the relative baseline systems for 30 s duration and 2.3% for 10 s and 3 s durations.