Exploring Data-Independent Dimensionality Reduction in Sparse Representation-Based Speaker Identification

The sparse representation classification (SRC) has attracted the attention of many signal processing domains in past few years. Recently, it has been successfully explored for the speaker recognition task with Gaussian mixture model (GMM) mean supervectors which are typically of the order of tens of thousands as speaker representations. As a result of this, the complexity of such systems become very high. With the use of the state-of-the-art i-vector representations, the dimension of GMM mean supervectors can be reduced effectively. But the i-vector approach involves a high dimensional data projection matrix which is learned using the factor analysis approach over huge amount of data from a large number of speakers. Also, the estimation of i-vector for a given utterance involves a computationally complex procedure. Motivated by these facts, we explore the use of data-independent projection approaches for reducing the dimensionality of GMM mean supervectors. The data-independent projection methods studied in this work include a normal random projection and two kinds of sparse random projections. The study is performed on SRC-based speaker identification using the NIST SRE 2005 dataset which includes channel matched and mismatched conditions. We find that the use of data-independent random projections for the dimensionality reduction of the supervectors results in only 3 % absolute loss in performance compared to that of the data-dependent (i-vector) approach. It is highlighted that with the use of highly sparse random projection matrices having 1 as non-zero coefficients, a significant reduction in computational complexity is achieved in finding the projections. Further, as these matrices do not require floating point representations, their storage requirement is also very small compared to that of the data-dependent or the normal random projection matrices. These reduced complexity sparse random projections would be of interest in context of the speaker recognition applications implemented on platforms having low computational power.