In this paper, we examine the use of Joint Factor Analysis methods on RSR2015 digits. A tied-mixture model is used for segmentation of the utterances into digits, while Joint Factor Analysis and a Joint Density model are deployed for features and backend, respectively. A novel approach for digit-dependent fusion of UBM-component log-likelihood ratios is introduced, yielding the best results so far. The fusion of 5 different JFA features gives an equal-error rate of 3.6%, compared to 6.3% attained by the a baseline GMM-UBM model with score normalization.
Bibliographic reference. Stafylakis, Themos / Kenny, Patrick / Alam, Md. Jahangir / Kockmann, Marcel (2015): "JFA for speaker recognition with random digit strings", In INTERSPEECH-2015, 190-194.