Gender-independent speaker recognition using source normalisation

Source-normalisation (SN) was proposed to improve the robustness of i-vector-based speaker recognition for under-resourced and unseen cross-speech-source evaluation conditions. The technique of source-normalisation estimates directions of undesired within-speaker variation more accurately than traditional methods when cross-source variation is not explicitly observed from each speaker in system development data. Incorporated into Within Class Covariance Normalisation (WCCN), source-normalisation provides significant improvements to speaker recognition based on i-vectors. This paper proposes a novel approach to gender-independent Probabilistic LDA (PLDA) through the use of SN-WCCN to normalise for the variation that separates genders as a pre-processing step for i-vector based PLDA classification. Evaluated on the NIST 2010 speaker recognition evaluation (SRE) dataset, the proposed approach demonstrated performance comparable to a typical gender-dependent configuration.