Decision trees with improved efficiency for fast speaker verification
Gilles Gonon, Rémi Gribonval, Frédéric Bimbot · 2005
Classification and regression trees (CART) are convenient for low complexity speaker recognition on embedded devices. However, former attempts at using trees performed quite poorly compared to state of the art results with Gaussian Mixture Mod-els (GMM). In this article, we introduce some solutions to im-prove the efficiency of the tree-based approach. First, we pro-pose to use at the tree construction level different types of infor-mation from the GMM used in state of the art techniques. Then, we model the score function within each leaf of the tree by a lin-ear score function. Considering a baseline state of the art system with an equal error rate (EER) of 8.6 % on the NIST 2003 eval-uation, a previous CART method provides typical EER ranging between 16 % and 18 % while the proposed improvements de-crease the EER to 11.5%, with a computational cost suitable for embedded devices. 1.