12th Annual Conference of the International Speech Communication Association

Florence, Italy
August 27-31. 2011

An Analysis Framework Based on Random Subspace Sampling for Speaker Verification

Weiwu Jiang (1), Zhifeng Li (2), Helen Meng (1)

(1) Chinese University of Hong Kong, China
(2) Chinese Academy of Sciences, China

Using Joint Factor Analysis (JFA) supervector for subspace analysis has many problems, such as high processing complexity and over-fitting. We propose an analysis framework based on random subspace sampling to address these problems. In this framework, JFA supervectors are first partitioned equally and each partitioned subvector is projected on to a subspace by PCA. All projected subvectors are then concatenated and PCA is applied again to reduce the dimension by projection onto a low-dimensional feature space. Finally, we randomly sample this feature space and build classifiers for the sampled features. The classifiers are fused to produce the final classification output. Experiments on NIST SRE08 prove the effectiveness of the proposed framework.

Full Paper

Bibliographic reference.  Jiang, Weiwu / Li, Zhifeng / Meng, Helen (2011): "An analysis framework based on random subspace sampling for speaker verification", In INTERSPEECH-2011, 253-256.