I-matrix for text-independent speaker recognition

This paper proposes an i-matrix for text-independent speaker recognition. The framework of the proposed i-matrix is similar to an i-vector. However, the presented method takes short-time cepstral feature matrices as inputs to explore both cepstral feature distribution and temporal information for the recognition task in the phase of statistical modeling. In the i-matrix, the variability of an utterance is constrained by two subspaces U and V, which are estimated by an iterative method on a large database. When U and V are well built, each utterance is represented by an i-matrix. Decision function is a cosine kernel. Experiments were carried out on the tel-tel-English condition of NIST SRE 2008 core task. Compared with an i-vector-LDA, the average EER and MDCF of an i-matrix-LDA showed a relative decrease of 4.82% and 5.12% respectively.