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.
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
@inproceedings{chen18j_interspeech,
author={Nanxin Chen and Jesús Villalba and Najim Dehak},
title={{An Investigation of Non-linear i-vectors for Speaker Verification}},
year=2018,
booktitle={Proc. Interspeech 2018},
pages={87--91},
doi={10.21437/Interspeech.2018-2474}
}