This paper deals the combination of nonlinear predictive models with classical LPCC parameterization for speaker recognition. It is shown that the combination of both a measure defined over LPCC coefficients and a measure defined over predictive analysis residual signal gives rise to an improvement over the classical method that considers only the LPCC coefficients. If the residual signal is obtained from a linear prediction analysis, the improvement is 2.63% (error rate drops from 6.31% to 3.68%) and if it is computed through a nonlinear predictive neural nets based model, the improvement is 3.68%. An efficient algorithm for reducing the computational burden is also proposed.
Cite as: Faúndez-Zanuy, M. (1999) Speaker recognition by means of a combination of linear and nonlinear predictive models. Proc. 6th European Conference on Speech Communication and Technology (Eurospeech 1999), 763-766, doi: 10.21437/Eurospeech.1999-185
@inproceedings{faundezzanuy99_eurospeech,
author={Marcos Faúndez-Zanuy},
title={{Speaker recognition by means of a combination of linear and nonlinear predictive models}},
year=1999,
booktitle={Proc. 6th European Conference on Speech Communication and Technology (Eurospeech 1999)},
pages={763--766},
doi={10.21437/Eurospeech.1999-185}
}