This paper proposes a probabilistic sequence kernel based on the universal background model, which is widely used in speaker recognition. The Gaussian components are used to construct the speaker reference space, and the utterances with different length are mapped into the fixed size vectors after normalization with correlation matrix. Finally the linear support vector machine is used for speaker recognition. A transition probabilistic sequence kernel is also proposed by adaption the transition information between neighbor frames. The experiments on NIST 2001 show that the performance is compared with the traditional UBM-MAP model. If we fusion the models, the performance will be improved 16.8% and 19.1% respectively compared with the UBM-MAP model.
Bibliographic reference. Lei, Zhenchun (2009): "UBM-based sequence kernel for speaker recognition", In INTERSPEECH-2009, 1279-1282.