Low-complexity speaker verification with decimated supervector representations

Abstract

This work explores the use of a few low-complexity data-independent projections for reducing the dimensionality of GMM supervectors in context of speaker verification (SV). The projections derived using sparse random matrix and decimation are explored and are used as speaker representations. The reported study is done on the NIST 2012 SRE task using a state-of-the-art PLDA based SV system. Interestingly, the systems incorporating the proposed projections result in performances competitive to that of the commonly used i-vector representation based one. Both the sparse random matrix and the decimation based approaches are attributed to have very low computational requirements in finding the speaker representations. A novel SV system that exploits the diversity among the representations obtained by using different offsets in the decimation of supervector, is also proposed. The resulted system is found to achieve a relative improvement of 7% in terms of both detection cost and equal error rate over the default i-vector based system while still having lesser overall complexity.