ISCA Archive Odyssey 2014
ISCA Archive Odyssey 2014

Text-Independent Speaker Verification via State Alignment

Zhi-Yi Li, Wei-Qiang Zhang, Wei-Wei Liu, Yao Tian, Jia Liu

To model the speech utterance at a finer granularity, this paper presents a novel state-alignment based supervector modeling method for text-independent speaker verification, which takes advantage of state-alignment method used in hidden Markov model (HMM) based acoustic modeling in speech recognition. By this way, the proposed modeling method can convert a text-independent speaker verification problem to a state-dependent one. Firstly, phoneme HMMs are trained. Then the clustered state Gaussian Mixture Models (GMM) is data-driven trained by the states of all phoneme HMMs. Next, the given speech utterance is modeled to sub-GMM supervectors in state level and be further aligned to be a final supervector. Besides, considering the duration differences between states, a weighting method is also proposed for kernel based support vector machine (SVM) classification. Experimental results in SRE 2008 core-core dataset show that the proposed methods outperform the traditional GMM supervector modeling followed by SVM (GSV-SVM), yielding relative 8.4% and 5.9% improvements of EER and minDCF, respectively.

doi: 10.21437/Odyssey.2014-10

Cite as: Li, Z.-Y., Zhang, W.-Q., Liu, W.-W., Tian, Y., Liu, J. (2014) Text-Independent Speaker Verification via State Alignment. Proc. The Speaker and Language Recognition Workshop (Odyssey 2014), 68-72, doi: 10.21437/Odyssey.2014-10

  author={Zhi-Yi Li and Wei-Qiang Zhang and Wei-Wei Liu and Yao Tian and Jia Liu},
  title={{Text-Independent Speaker Verification via State Alignment}},
  booktitle={Proc. The Speaker and Language Recognition Workshop (Odyssey 2014)},