Improving PLDA speaker verification using WMFD and linear-weighted approaches in limited microphone data conditions

摘要

This paper proposes the addition of a weighted median Fisheruddiscriminator (WMFD) projection prior to length-normalisedudGaussian probabilistic linear discriminant analysis (GPLDA)udmodelling in order to compensate the additional session variation. In limited microphone data conditions, a linear-weighted approach is introduced to increase the influence of microphone speech dataset. The linear-weighted WMFD-projected GPLDA system shows improvements in EER and DCF values over the pooled LDA- and WMFD-projected GPLDA systems in inter-view-interview condition as WMFD projection extracts more speaker discriminant information with limited number of sessions/ speaker data, and linear-weighted GPLDA approach estimates reliable model parameters with limited microphone data.