A tree-based kernel selection approach to efficient Gaussian mixture model–universal background model based speaker identification

We propose a tree-based kernel selection (TBKS) algorithm as a computationally efficient approach to the Gaussian mixture model–universal background model (GMM–UBM) based speaker identification. All Gaussian components in the universal background model are first clustered hierarchically into a tree and the corresponding acoustic space is mapped into structurally partitioned regions. When identifying a speaker, each test input feature vector is scored against a small subset of all Gaussian components. As a result of this TBKS process, computation complexity can be significantly reduced. We improve the efficiency of the proposed system further by applying a previously proposed observation reordering based pruning (ORBP) to screen out unlikely candidate speakers. The approach is evaluated on a speech database of 1031 speakers, in both clean and noisy conditions. The experimental results show that by integrating TBKS and ORBP together we can speed up the computation efficiency by a factor of 15.8 with only a very slight degradation of identification performance, i.e., an increase of 1% of relative error rate, compared with a baseline GMM–UBM system. The improved search efficiency is also robust to additive noise.