An Investigation of Non-linear i-vectors for Speaker Verification

Nanxin Chen, Jesús Villalba, Najim Dehak

Speaker verification becomes increasingly important due to the popularity of speech assistants and smart home. i-vectors are used broadly for this topic, which use factor analysis to model the shift of average parameter in Gaussian Mixture Models. Recently by the progress of deep learning, high-level non-linearity improves results in many areas. In this paper we proposed a new framework of i-vectors which uses stochastic gradient descent to solve the problem of i-vectors. From our preliminary results stochastic gradient descent can get same performance as expectation-maximization algorithm. However, by backpropagation the assumption can be more flexible, so both linear and non-linear assumption is possible in our framework. From our result, both maximum a posteriori estimation and maximum likelihood lead to slightly better result than conventional i-vectors and both linear and non-linear system has similar performance.

 DOI: 10.21437/Interspeech.2018-2474

Cite as: Chen, N., Villalba, J., Dehak, N. (2018) An Investigation of Non-linear i-vectors for Speaker Verification. Proc. Interspeech 2018, 87-91, DOI: 10.21437/Interspeech.2018-2474.

  author={Nanxin Chen and Jesús Villalba and Najim Dehak},
  title={An Investigation of Non-linear i-vectors for Speaker Verification},
  booktitle={Proc. Interspeech 2018},