A new fast and memory effective i-vector extraction based on factor analysis of KLD derived GMM supervector

At present, i-vector model has become the state-of-the-art technology for speaker recognition. It represents speech utterance to a low-dimensional fix-length compact i-vector. For some real application, i-vector extraction procedure is relatively slow and requires too much memories. Some numerical approximation based fast extraction methods have been proposed to speed up the computation and to save memory meanwhile. However they are all at the expense of more or less performance degradation. From a novel model approximation viewpoint, we first propose a novel fast i-vector extraction method based on subspace factor analysis from Kullback-Leibler divergence derived Gaussian Mixture Models supervector. Experimental results on NIST SRE datasets demonstrate that the proposed method is more faster and performs more better than all the existing methods at the similar run time ratio. Besides, due to the different modeling viewpoint, we proposed a combination method with factorized subspace based extraction. This method can avoid the accuracy degradation and even can perform better than the standard one, while its extraction speed can be 10 times faster than the standard method.