ISCA Archive Odyssey 2014
ISCA Archive Odyssey 2014

Local Variability Modeling for Text-Independent Speaker Verification

Kong Aik Lee, Bin Ma, Haizhou Li, Liping Chen, Wu Guo, Lirong Dai

Total variability model (TVM) was recently proposed for the com-pression of speech utterances to low dimensional vectors (i.e., the so-call identity vector or i-vector). Compared to the variable-length nature of the speech utterances, the i-vectors have fixed length and therefore could be used with simple classifier for text-independent speaker verification task. This paper proposes the local variability model (LVM) the central idea of which is to capture the local vari-ability associated with individual Gaussians in the acoustic space that are absent in the i-vector representation. We analyze the latent structure of both the total and local variability model and show that parameter tying across mixtures leads to powerful methods for information extraction. Experimental results on NIST SRE’08 and SRE’10 datasets show that the proposed LVM is effective for speaker verification.


doi: 10.21437/Odyssey.2014-11

Cite as: Lee, K.A., Ma, B., Li, H., Chen, L., Guo, W., Dai, L. (2014) Local Variability Modeling for Text-Independent Speaker Verification. Proc. The Speaker and Language Recognition Workshop (Odyssey 2014), 54-59, doi: 10.21437/Odyssey.2014-11

@inproceedings{lee14_odyssey,
  author={Kong Aik Lee and Bin Ma and Haizhou Li and Liping Chen and Wu Guo and Lirong Dai},
  title={{Local Variability Modeling for Text-Independent Speaker Verification}},
  year=2014,
  booktitle={Proc. The Speaker and Language Recognition Workshop (Odyssey 2014)},
  pages={54--59},
  doi={10.21437/Odyssey.2014-11}
}